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featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF

featherless-ai-quants

text-generation

--- base_model: ChaoticNeutrals/Hathor_Respawn-L3-8B-v0.8 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # ChaoticNeutrals/Hathor_Respawn-L3-8B-v0.8 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q8_0.gguf) | 8145.11 MB | | Q4_K_S | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q4_K_S.gguf) | 4475.28 MB | | Q2_K | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q2_K.gguf) | 3031.86 MB | | Q6_K | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q6_K.gguf) | 6290.44 MB | | Q3_K_M | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_S.gguf) | 3494.74 MB | | Q3_K_L | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q3_K_L.gguf) | 4121.74 MB | | Q4_K_M | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q4_K_M.gguf) | 4692.78 MB | | Q5_K_S | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q5_K_S.gguf) | 5339.90 MB | | Q5_K_M | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-Q5_K_M.gguf) | 5467.40 MB | | IQ4_XS | [ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-GGUF/blob/main/ChaoticNeutrals-Hathor_Respawn-L3-8B-v0.8-IQ4_XS.gguf) | 4276.62 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

jmalejandrob79/cndnlsldds

jmalejandrob79

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF

featherless-ai-quants

text-generation

--- base_model: NExtNewChattingAI/shark_tank_ai_7b_v2 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # NExtNewChattingAI/shark_tank_ai_7b_v2 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q8_0.gguf) | 7339.34 MB | | Q4_K_S | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q4_K_S.gguf) | 3948.57 MB | | Q2_K | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q2_K.gguf) | 2593.27 MB | | Q6_K | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q6_K.gguf) | 5666.79 MB | | Q3_K_M | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_S.gguf) | 3017.97 MB | | Q3_K_L | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q3_K_L.gguf) | 3644.97 MB | | Q4_K_M | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q4_K_M.gguf) | 4166.07 MB | | Q5_K_S | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q5_K_S.gguf) | 4766.19 MB | | Q5_K_M | [NExtNewChattingAI-shark_tank_ai_7b_v2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-Q5_K_M.gguf) | 4893.69 MB | | IQ4_XS | [NExtNewChattingAI-shark_tank_ai_7b_v2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/NExtNewChattingAI-shark_tank_ai_7b_v2-GGUF/blob/main/NExtNewChattingAI-shark_tank_ai_7b_v2-IQ4_XS.gguf) | 3761.66 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

featherless-ai-quants/Gryphe-MythoMist-7b-GGUF

featherless-ai-quants

text-generation

--- base_model: Gryphe/MythoMist-7b pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # Gryphe/MythoMist-7b GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [Gryphe-MythoMist-7b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q8_0.gguf) | 7339.34 MB | | Q4_K_S | [Gryphe-MythoMist-7b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q4_K_S.gguf) | 3948.57 MB | | Q2_K | [Gryphe-MythoMist-7b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q2_K.gguf) | 2593.27 MB | | Q6_K | [Gryphe-MythoMist-7b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q6_K.gguf) | 5666.79 MB | | Q3_K_M | [Gryphe-MythoMist-7b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [Gryphe-MythoMist-7b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q3_K_S.gguf) | 3017.97 MB | | Q3_K_L | [Gryphe-MythoMist-7b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q3_K_L.gguf) | 3644.97 MB | | Q4_K_M | [Gryphe-MythoMist-7b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q4_K_M.gguf) | 4166.07 MB | | Q5_K_S | [Gryphe-MythoMist-7b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q5_K_S.gguf) | 4766.19 MB | | Q5_K_M | [Gryphe-MythoMist-7b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-Q5_K_M.gguf) | 4893.69 MB | | IQ4_XS | [Gryphe-MythoMist-7b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-MythoMist-7b-GGUF/blob/main/Gryphe-MythoMist-7b-IQ4_XS.gguf) | 3761.66 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

featherless-ai-quants/bunnycore-Cognitron-8B-GGUF

featherless-ai-quants

text-generation

--- base_model: bunnycore/Cognitron-8B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # bunnycore/Cognitron-8B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [bunnycore-Cognitron-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q8_0.gguf) | 8145.11 MB | | Q4_K_S | [bunnycore-Cognitron-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q4_K_S.gguf) | 4475.28 MB | | Q2_K | [bunnycore-Cognitron-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q2_K.gguf) | 3031.86 MB | | Q6_K | [bunnycore-Cognitron-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q6_K.gguf) | 6290.44 MB | | Q3_K_M | [bunnycore-Cognitron-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [bunnycore-Cognitron-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q3_K_S.gguf) | 3494.74 MB | | Q3_K_L | [bunnycore-Cognitron-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q3_K_L.gguf) | 4121.74 MB | | Q4_K_M | [bunnycore-Cognitron-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q4_K_M.gguf) | 4692.78 MB | | Q5_K_S | [bunnycore-Cognitron-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q5_K_S.gguf) | 5339.90 MB | | Q5_K_M | [bunnycore-Cognitron-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-Q5_K_M.gguf) | 5467.40 MB | | IQ4_XS | [bunnycore-Cognitron-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/bunnycore-Cognitron-8B-GGUF/blob/main/bunnycore-Cognitron-8B-IQ4_XS.gguf) | 4276.62 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

Oleg1231gelO/Doome

Oleg1231gelO

Entry not found

DavidAU/MN-WORDSTORM-pt8-RCM-Emotion-Action-18.5B-Instruct

DavidAU

transformers

text-generation

--- library_name: transformers tags: - mergekit - merge base_model: [] --- <h2>MN-WORDSTORM-pt8-RCM-Into-Darkness-18.5B-Instruct</h2> This is part 8 in a 10 part series. This version's highlights (relative to others in this 10 part series): "Order of action within a scene is excellent, command of emotion shows in prose/characters." (PPL = 7.7857 +/- 0.12683 @ Q4KM) Note this model is the core model used in the new "DARKEST UNIVERSE 29B" connected with V2 of the Brainstorm 40x adapter which yeilds a model of 112 layers, 921 tensors. A beast of a creative model that excels at all levels of creativity. Examples on the model card below: [ https://huggingface.co/DavidAU/MN-DARKEST-UNIVERSE-29B-GGUF ] This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. For full information about this model, including: - Details about this model and its use case(s). - Context limits - Special usage notes / settings. - Any model(s) used to create this model. - Template(s) used to access/use this model. - Example generation(s) - GGUF quants of this model GGUF quants of this version will follow shortly and appear on this page. Settings, Templates, Context limits etc etc are the same for all 10 in the series, you can view any below for "part 8's" information. Also, each of the 5 below include example generations which will indicate in part this "parts" generation abilities. However there will be variations which is what this 10 parts series is really all about. For more information on this 10 part series see one or more of these versions: [ https://huggingface.co/DavidAU/MN-WORDSTORM-pt1-RCM-Kiss-of-Madness-18.5B-Instruct-GGUF ] [ https://huggingface.co/DavidAU/MN-WORDSTORM-pt2-RCM-Escape-Room-18.5B-Instruct-GGUF ] [ https://huggingface.co/DavidAU/MN-WORDSTORM-pt3-RCM-POV-Nightmare-18.5B-Instruct-GGUF ] [ https://huggingface.co/DavidAU/MN-WORDSTORM-pt4-RCM-Cliffhanger-18.5B-Instruct-GGUF ] [ https://huggingface.co/DavidAU/MN-WORDSTORM-pt5-RCM-Extra-Intense-18.5B-Instruct-gguf ]

Dheeraj46329/llama-3.2-new-18-0.5-3e-warmup

Dheeraj46329

--- license: llama3.2 ---

wesley157kkkkkk/lesley

wesley157kkkkkk

Entry not found

shopitalic/camila-cinnamon-cashmere-collared-sweater-rafael

shopitalic

diffusers

text-to-image

--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # camila cinnamon cashmere collared sweater rafael <Gallery /> ## Model description ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/shopitalic/camila-cinnamon-cashmere-collared-sweater-rafael/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).

LinxuanPastel/gigante

LinxuanPastel

Entry not found

mradermacher/Faya-Expanse-8B-i1-GGUF

mradermacher

--- base_model: Svngoku/Faya-Expanse-8B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Svngoku/Faya-Expanse-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Faya-Expanse-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.3 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.5 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 4.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.9 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.9 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.9 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Faya-Expanse-8B-i1-GGUF/resolve/main/Faya-Expanse-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

ZYMScott/mRNAdesigner

ZYMScott

Entry not found

vedal-ai/azure-speech

vedal-ai

Entry not found

onnx-community/MobileLLM-350M

onnx-community

transformers.js

text-generation

--- library_name: transformers.js base_model: facebook/MobileLLM-350M --- https://huggingface.co/facebook/MobileLLM-350M with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

glif-loradex-trainer/x_bulbul_x_windows_95_UI

glif-loradex-trainer

diffusers

text-to-image

--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1730421901473__000003000_0.jpg text: wounded centaur, mythical creature, windows 95 - output: url: samples/1730421925134__000003000_1.jpg text: ruins of athens, snake, windows 95 - output: url: samples/1730421948621__000003000_2.jpg text: silver vampire sword, windows 95 - output: url: samples/1730421972112__000003000_3.jpg text: mspaint with starry night, windows 95 - output: url: samples/1730421995723__000003000_4.jpg text: sonic game, windows 95 base_model: black-forest-labs/FLUX.1-dev trigger: windows 95 instance_prompt: windows 95 license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # windows_95_UI Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `x_bulbul_x`. <Gallery /> ## Trigger words You should use `windows 95` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/x_bulbul_x_windows_95_UI/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

nikutd01/emotion_tweet_albert-base-v2_2024-11-01

nikutd01

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MaziyarPanahi/Chili_Dog_8B-GGUF

MaziyarPanahi

text-generation

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: Chili_Dog_8B-GGUF base_model: FourOhFour/Chili_Dog_8B inference: false model_creator: FourOhFour pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Chili_Dog_8B-GGUF](https://huggingface.co/MaziyarPanahi/Chili_Dog_8B-GGUF) - Model creator: [FourOhFour](https://huggingface.co/FourOhFour) - Original model: [FourOhFour/Chili_Dog_8B](https://huggingface.co/FourOhFour/Chili_Dog_8B) ## Description [MaziyarPanahi/Chili_Dog_8B-GGUF](https://huggingface.co/MaziyarPanahi/Chili_Dog_8B-GGUF) contains GGUF format model files for [FourOhFour/Chili_Dog_8B](https://huggingface.co/FourOhFour/Chili_Dog_8B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

onnx-community/MobileLLM-600M

onnx-community

transformers.js

text-generation

--- library_name: transformers.js base_model: facebook/MobileLLM-600M --- https://huggingface.co/facebook/MobileLLM-600M with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Nisk36/finetuned-augmxnt_shisa-gamma-7b-v1

Nisk36

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Houssem-Karboul/BRAIN_TUMOR_CLASSIFICATION

Houssem-Karboul

tf-keras

Entry not found

GalacticLad/Guilty_Gear_SDXL

GalacticLad

Entry not found

mhnakif/flux-turbo-full

mhnakif

Entry not found

onnx-community/MobileLLM-1B

onnx-community

transformers.js

text-generation

--- library_name: transformers.js base_model: facebook/MobileLLM-1B --- https://huggingface.co/facebook/MobileLLM-1B with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

ha684/haocr_V1.1

ha684

transformers

image-text-to-text

Invalid username or password.

AlignmentResearch/robust_llm_pythia-6.9b_clf_harmless_v-ian-135c_s-0

AlignmentResearch

Entry not found

piotrekgrl/llama381binstruct_summarize_short

piotrekgrl

peft

--- base_model: NousResearch/Meta-Llama-3.1-8B-Instruct datasets: - generator library_name: peft license: llama3.1 tags: - trl - sft - generated_from_trainer model-index: - name: llama381binstruct_summarize_short results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama381binstruct_summarize_short This model is a fine-tuned version of [NousResearch/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.4181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5775 | 0.25 | 5 | 1.5695 | | 1.7351 | 0.5 | 10 | 1.3455 | | 2.0074 | 0.75 | 15 | 1.2429 | | 1.6972 | 1.0 | 20 | 1.1852 | | 1.2054 | 1.25 | 25 | 1.1672 | | 1.4255 | 1.5 | 30 | 1.1778 | | 0.9758 | 1.75 | 35 | 1.1446 | | 1.3851 | 2.0 | 40 | 1.1498 | | 0.8252 | 2.25 | 45 | 1.1879 | | 1.0266 | 2.5 | 50 | 1.2851 | | 0.6106 | 2.75 | 55 | 1.2537 | | 0.9328 | 3.0 | 60 | 1.2100 | | 0.5083 | 3.25 | 65 | 1.2748 | | 0.4762 | 3.5 | 70 | 1.4306 | | 0.7648 | 3.75 | 75 | 1.4550 | | 0.2807 | 4.0 | 80 | 1.3928 | | 0.3343 | 4.25 | 85 | 1.3819 | | 0.4685 | 4.5 | 90 | 1.3942 | | 0.1421 | 4.75 | 95 | 1.4113 | | 0.2701 | 5.0 | 100 | 1.4181 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.1

jeffmeloy/Qwen-7B-nerd-uncensored-v1.1

jeffmeloy

Temporary Redirect. Redirecting to /jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.1/resolve/main/README.md

0xIbra/flux.1-dev-hyper

0xIbra

diffusers

text-to-image

--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ArikAranta/marian-finetuned-kde4-en-to-fr

ArikAranta

Entry not found

piotrekgrl/llama381binstruct_summarize_short_merged

piotrekgrl

transformers

text-generation

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Daverick/Kairo

Daverick

Entry not found

autoprogrammer/CulturaX-zh-unsupervised-2000

autoprogrammer

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

onnx-community/OmniParser-icon_detect

onnx-community

Entry not found

septyoa/LaptopPricePredv4

septyoa

Entry not found

onnx-community/OmniParser-icon_detect_640x640

onnx-community

Entry not found

mradermacher/Llama-3.2-3B-Apex-i1-GGUF

mradermacher

transformers

--- base_model: bunnycore/Llama-3.2-3B-Apex language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/bunnycore/Llama-3.2-3B-Apex <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ1_S.gguf) | i1-IQ1_S | 1.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ1_M.gguf) | i1-IQ1_M | 1.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ2_S.gguf) | i1-IQ2_S | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ2_M.gguf) | i1-IQ2_M | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q2_K.gguf) | i1-Q2_K | 1.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ3_S.gguf) | i1-IQ3_S | 1.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ3_M.gguf) | i1-IQ3_M | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.0 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.1 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 2.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 2.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 2.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_0.gguf) | i1-Q4_0 | 2.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Apex-i1-GGUF/resolve/main/Llama-3.2-3B-Apex.i1-Q6_K.gguf) | i1-Q6_K | 3.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

ArikAranta/marian-finetuned-kde4-en-to-id

ArikAranta

Entry not found

davidrd123/Flux-Rich-Sullivan-Staged-LoKr

davidrd123

Invalid username or password.

luckycontrol/chatbot

luckycontrol

Entry not found

jncraton/SmolLM2-360M-Instruct-ct2-int8

jncraton

transformers

--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2  ## Table of Contents 1. [Model Summary](##model-summary) 2. [Limitations](##limitations) 3. [Training](##training) 4. [License](##license) 5. [Citation](##citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 360M model was trained on 4 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-360M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model | Metrics | SmolLM2-360M | Qwen2.5-0.5B | SmolLM-360M | |:-------------------|:------------:|:------------:|:------------:| | HellaSwag | **54.5** | 51.2 | 51.8 | | ARC (Average) | **53.0** | 45.4 | 50.1 | | PIQA | **71.7** | 69.9 | 71.6 | | MMLU (cloze) | **35.8** | 33.7 | 34.4 | | CommonsenseQA | **38.0** | 31.6 | 35.3 | | TriviaQA | **16.9** | 4.3 | 9.1 | | Winogrande | 52.5 | **54.1** | 52.8 | | OpenBookQA | **37.4** | **37.4** | 37.2 | | GSM8K (5-shot) | 3.2 | **33.4** | 1.6 | ## Instruction Model | Metric | SmolLM2-360M-Instruct | Qwen2.5-0.5B-Instruct | SmolLM-360M-Instruct | |:-----------------------------|:---------------------:|:---------------------:|:---------------------:| | IFEval (Average prompt/inst) | **41.0** | 31.6 | 19.8 | | MT-Bench | 3.66 | **4.16** | 3.37 | | HellaSwag | **52.1** | 48.0 | 47.9 | | ARC (Average) | **43.7** | 37.3 | 38.8 | | PIQA | **70.8** | 67.2 | 69.4 | | MMLU (cloze) | **32.8** | 31.7 | 30.6 | | BBH (3-shot) | 27.3 | **30.7** | 24.4 | | GSM8K (5-shot) | 7.43 | **26.8** | 1.36 | ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 4T - **Precision:** bfloat16 ### Hardware - **GPUs:** 64 H100 ### Software - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

juliekallini/my-awesome-model

juliekallini

Entry not found

straykittycat/straycat

straykittycat

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

straykittycat/straycatz

straykittycat

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Mercuri/boyelijah

Mercuri

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: boyelijah --- # Boyelijah <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `boyelijah` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Mercuri/boyelijah', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

glif-loradex-trainer/mesonwarrior_flux_dev_close_up_animals

glif-loradex-trainer

diffusers

text-to-image

--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1730423169560__000002000_0.jpg text: zebra - output: url: samples/1730423194113__000002000_1.jpg text: shark - output: url: samples/1730423218657__000002000_2.jpg text: tiger base_model: black-forest-labs/FLUX.1-dev trigger: close-up shot instance_prompt: close-up shot license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # flux_dev_close_up_animals Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `mesonwarrior`. <Gallery /> ## Trigger words You should use `close-up shot` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/mesonwarrior_flux_dev_close_up_animals/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

jncraton/SmolLM2-1.7B-Instruct-ct2-int8

jncraton

transformers

--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2  ## Table of Contents 1. [Model Summary](#model-summary) 2. [Evaluation](#evaluation) 3. [Examples](#examples) 4. [Limitations](#limitations) 5. [Training](#training) 6. [License](#license) 7. [Citation](#citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model | Metric | SmolLM2-1.7B | Llama-1B | Qwen2.5-1.5B | SmolLM1-1.7B | |------------------|--------------|-------------|---------------|--------------| | HellaSwag | **68.7** | 61.2 | 66.4 | 62.9 | | ARC (Average) | **60.5** | 49.2 | 58.5 | 59.9 | | PIQA | **77.6** | 74.8 | 76.1 | 76.0 | | MMLU-Pro (MCF) | **19.4** | 11.7 | 13.7 | 10.8 | | CommonsenseQA | **43.6** | 41.2 | 34.1 | 38.0 | | TriviaQA | **36.7** | 28.1 | 20.9 | 22.5 | | Winogrande | **59.4** | 57.8 | 59.3 | 54.7 | | OpenBookQA | 42.2 | 38.4 | 40.0 | **42.4** | | GSM8K (5-shot) | 31.0 | 7.2 | **61.3** | 5.5 | ## Instruction Model | Metric | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct | |:-----------------------------|:---------------------:|:-----------------:|:----------------------:|:----------------------:| | IFEval (Average prompt/inst) | **56.7** | 53.5 | 47.4 | 23.1 | | MT-Bench | 6.13 | 5.48 | **6.52** | 4.33 | | OpenRewrite-Eval (micro_avg RougeL) | 44.9 | 39.2 | **46.9** | NaN | | HellaSwag | **66.1** | 56.1 | 60.9 | 55.5 | | ARC (Average) | **51.7** | 41.6 | 46.2 | 43.7 | | PIQA | **74.4** | 72.3 | 73.2 | 71.6 | | MMLU-Pro (MCF) | 19.3 | 12.7 | **24.2** | 11.7 | | BBH (3-shot) | 32.2 | 27.6 | **35.3** | 25.7 | | GSM8K (5-shot) | **48.2** | 26.8 | 42.8 | 4.62 | ## Examples Below are some system and instruct prompts that work well for special tasks ### Text rewriting ```python system_prompt_rewrite = "You are an AI writing assistant. Your task is to rewrite the user's email to make it more professional and approachable while maintaining its main points and key message. Do not return any text other than the rewritten message." user_prompt_rewrite = "Rewrite the message below to make it more friendly and approachable while maintaining its main points and key message. Do not add any new information or return any text other than the rewritten message\nThe message:" messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content":f"{user_prompt_rewrite} The CI is failing after your last commit!}"] input_text=tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ``` Hey there! I noticed that the CI isn't passing after your latest commit. Could you take a look and let me know what's going on? Thanks so much for your help! ``` ### Summarization ```python system_prompt_summarize = "Provide a concise, objective summary of the input text in up to three sentences, focusing on key actions and intentions without using second or third person pronouns." messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content": INSERT_LONG_EMAIL] input_text=tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Function calling SmolLM2-1.7B-Instruct can handle function calling, it scores 27% on the [BFCL Leaderboard](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html). Here's how you can leverage it: ```python import json import re from typing import Optional from jinja2 import Template import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.utils import get_json_schema system_prompt = Template("""You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the functions can be used, point it out and refuse to answer. If the given question lacks the parameters required by the function, also point it out. You have access to the following tools: <tools>{{ tools }}</tools> The output MUST strictly adhere to the following format, and NO other text MUST be included. The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]'. <tool_call>[ {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}, ... (more tool calls as required) ]</tool_call>""") def prepare_messages( query: str, tools: Optional[dict[str, any]] = None, history: Optional[list[dict[str, str]]] = None ) -> list[dict[str, str]]: """Prepare the system and user messages for the given query and tools. Args: query: The query to be answered. tools: The tools available to the user. Defaults to None, in which case if a list without content will be passed to the model. history: Exchange of messages, including the system_prompt from the first query. Defaults to None, the first message in a conversation. """ if tools is None: tools = [] if history: messages = history.copy() messages.append({"role": "user", "content": query}) else: messages = [ {"role": "system", "content": system_prompt.render(tools=json.dumps(tools))}, {"role": "user", "content": query} ] return messages def parse_response(text: str) -> str | dict[str, any]: """Parses a response from the model, returning either the parsed list with the tool calls parsed, or the model thought or response if couldn't generate one. Args: text: Response from the model. """ pattern = r"<tool_call>(.*?)</tool_call>" matches = re.findall(pattern, text, re.DOTALL) if matches: return json.loads(matches[0]) return text ``` ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 11T - **Precision:** bfloat16 ### Hardware - **GPUs:** 256 H100 ### Software - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main) - **Alignement Handbook** [alignement-handbook](https://github.com/huggingface/alignment-handbook/) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

saqqdy/Qwen-Qwen1.5-0.5B-1730423292

saqqdy

Entry not found

Lekhansh/Llama-3.1-70B-Instruct-mixed-instructions

Lekhansh

Entry not found

onnx-community/BackgroundMattingV2-hd

onnx-community

Entry not found

onnx-community/BackgroundMattingV2-4k

onnx-community

Entry not found

hazzzz/sentiment-analysis-portuguese

hazzzz

Entry not found

raaedk/anime-style

raaedk

Entry not found

autoprogrammer/CulturaX-zh-unsupervised-2

autoprogrammer

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vnthuan02/HuggingFaceTesting

vnthuan02

Entry not found

4yo1/fine-pre3_lora4_1024-math10k-EL30k-INST0930-ep3_datacleanAXOLOTL-good

4yo1

Entry not found

straykittycat/straycatzz

straykittycat

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

kh4dien/gemma-politics-education-balanced

kh4dien

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF

featherless-ai-quants

text-generation

--- base_model: Locutusque/Hercules-3.0-Mistral-7B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # Locutusque/Hercules-3.0-Mistral-7B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [Locutusque-Hercules-3.0-Mistral-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q8_0.gguf) | 7339.34 MB | | Q4_K_S | [Locutusque-Hercules-3.0-Mistral-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q4_K_S.gguf) | 3948.57 MB | | Q2_K | [Locutusque-Hercules-3.0-Mistral-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q2_K.gguf) | 2593.27 MB | | Q6_K | [Locutusque-Hercules-3.0-Mistral-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q6_K.gguf) | 5666.80 MB | | Q3_K_M | [Locutusque-Hercules-3.0-Mistral-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [Locutusque-Hercules-3.0-Mistral-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q3_K_S.gguf) | 3017.97 MB | | Q3_K_L | [Locutusque-Hercules-3.0-Mistral-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q3_K_L.gguf) | 3644.97 MB | | Q4_K_M | [Locutusque-Hercules-3.0-Mistral-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q4_K_M.gguf) | 4166.07 MB | | Q5_K_S | [Locutusque-Hercules-3.0-Mistral-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q5_K_S.gguf) | 4766.19 MB | | Q5_K_M | [Locutusque-Hercules-3.0-Mistral-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-Q5_K_M.gguf) | 4893.69 MB | | IQ4_XS | [Locutusque-Hercules-3.0-Mistral-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hercules-3.0-Mistral-7B-GGUF/blob/main/Locutusque-Hercules-3.0-Mistral-7B-IQ4_XS.gguf) | 3761.66 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF

featherless-ai-quants

text-generation

--- base_model: CorticalStack/neurotic-crown-clown-7b-tak-stack-dpo pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # CorticalStack/neurotic-crown-clown-7b-tak-stack-dpo GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q8_0.gguf) | 7339.34 MB | | Q4_K_S | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q4_K_S.gguf) | 3948.57 MB | | Q2_K | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q2_K.gguf) | 2593.27 MB | | Q6_K | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q6_K.gguf) | 5666.80 MB | | Q3_K_M | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_S.gguf) | 3017.97 MB | | Q3_K_L | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q3_K_L.gguf) | 3644.97 MB | | Q4_K_M | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q4_K_M.gguf) | 4166.07 MB | | Q5_K_S | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q5_K_S.gguf) | 4766.19 MB | | Q5_K_M | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-Q5_K_M.gguf) | 4893.69 MB | | IQ4_XS | [CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-GGUF/blob/main/CorticalStack-neurotic-crown-clown-7b-tak-stack-dpo-IQ4_XS.gguf) | 3761.66 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

Kerneld/roberta-base-klue-ynat-classification

Kerneld

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/NoFo-codellama-13b-v0.1-GGUF

mradermacher

--- base_model: GWK/NoFo-codellama-13b-v0.1 language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/GWK/NoFo-codellama-13b-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NoFo-codellama-13b-v0.1-GGUF/resolve/main/NoFo-codellama-13b-v0.1.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

mradermacher/M-SOLAR-10.7B-v1.4-GGUF

mradermacher

--- base_model: megastudyedu/M-SOLAR-10.7B-v1.4 language: - ko library_name: transformers license: cc-by-nc-nd-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.4 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/M-SOLAR-10.7B-v1.4-GGUF/resolve/main/M-SOLAR-10.7B-v1.4.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

vnthuan02/HuggingTesting

vnthuan02

--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - av base_model: - openai/whisper-large-v3-turbo ---

yoste/Como_Se_Llama

yoste

--- license: llama3.2 ---

featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF

featherless-ai-quants

--- base_model: cosmicvalor/mistral-orthogonalized pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # cosmicvalor/mistral-orthogonalized GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [cosmicvalor-mistral-orthogonalized-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q8_0.gguf) | 7339.34 MB | | Q4_K_S | [cosmicvalor-mistral-orthogonalized-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q4_K_S.gguf) | 3948.57 MB | | Q2_K | [cosmicvalor-mistral-orthogonalized-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q2_K.gguf) | 2593.27 MB | | Q6_K | [cosmicvalor-mistral-orthogonalized-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q6_K.gguf) | 5666.80 MB | | Q3_K_M | [cosmicvalor-mistral-orthogonalized-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [cosmicvalor-mistral-orthogonalized-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q3_K_S.gguf) | 3017.97 MB | | Q3_K_L | [cosmicvalor-mistral-orthogonalized-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q3_K_L.gguf) | 3644.97 MB | | Q4_K_M | [cosmicvalor-mistral-orthogonalized-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q4_K_M.gguf) | 4166.07 MB | | Q5_K_S | [cosmicvalor-mistral-orthogonalized-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q5_K_S.gguf) | 4766.19 MB | | Q5_K_M | [cosmicvalor-mistral-orthogonalized-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-Q5_K_M.gguf) | 4893.69 MB | | IQ4_XS | [cosmicvalor-mistral-orthogonalized-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/cosmicvalor-mistral-orthogonalized-GGUF/blob/main/cosmicvalor-mistral-orthogonalized-IQ4_XS.gguf) | 3761.66 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

straykittycat/straycats

straykittycat

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

saqqdy/Qwen-Qwen1.5-1.8B-1730423943

saqqdy

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.1

matthewlee23/lora-test

matthewlee23

Entry not found

minimimtoy25/tcross

minimimtoy25

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

mtzig/test

mtzig

Entry not found

Kort/i82

Kort

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kromquant/testing-try-003-GGUFs

kromquant

Invalid username or password.

DevQuasar/HuggingFaceTB.SmolLM2-135M-Instruct-GGUF

DevQuasar

--- base_model: - HuggingFaceTB/SmolLM2-135M-Instruct ---

DevQuasar/HuggingFaceTB.SmolLM2-360M-Instruct-GGUF

DevQuasar

--- base_model: - HuggingFaceTB/SmolLM2-360M-Instruct ---

Nitral-AI/Captain_BMO-12B

Nitral-AI

Entry not found

mradermacher/llama-2-7b-Amharic-pretrained-GGUF

mradermacher

--- base_model: AbelBekele/llama-2-7b-Amharic-pretrained language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AbelBekele/llama-2-7b-Amharic-pretrained <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q3_K_S.gguf) | Q3_K_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q6_K.gguf) | Q6_K | 5.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-7b-Amharic-pretrained-GGUF/resolve/main/llama-2-7b-Amharic-pretrained.f16.gguf) | f16 | 13.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

featherless-ai-quants/KnutJaegersberg-Deita-32b-GGUF

featherless-ai-quants

Entry not found

mradermacher/lumimaid-8B-autotrain-i1-GGUF

mradermacher

--- base_model: mrcuddle/lumimaid-8B-autotrain datasets: - mpasila/Literotica-stories-short-json-unfiltered - Chadgpt-fam/sexting_dataset language: - en library_name: transformers license: other quantized_by: mradermacher tags: - autotrain - text-generation-inference - text-generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mrcuddle/lumimaid-8B-autotrain <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/lumimaid-8B-autotrain-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/lumimaid-8B-autotrain-i1-GGUF/resolve/main/lumimaid-8B-autotrain.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

DevQuasar/HuggingFaceTB.SmolLM2-1.7B-Instruct-GGUF

DevQuasar

--- base_model: - HuggingFaceTB/SmolLM2-1.7B-Instruct ---

saqqdy/Qwen-Qwen1.5-0.5B-1730424544

saqqdy

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.1

featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF

featherless-ai-quants

--- base_model: ContextualAI/archangel_sft-kto_llama13b pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # ContextualAI/archangel_sft-kto_llama13b GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [ContextualAI-archangel_sft-kto_llama13b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q8_0.gguf) | 13190.57 MB | | Q4_K_S | [ContextualAI-archangel_sft-kto_llama13b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q4_K_S.gguf) | 7079.30 MB | | Q2_K | [ContextualAI-archangel_sft-kto_llama13b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q2_K.gguf) | 4629.39 MB | | Q6_K | [ContextualAI-archangel_sft-kto_llama13b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q6_K.gguf) | 10184.42 MB | | Q3_K_M | [ContextualAI-archangel_sft-kto_llama13b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q3_K_M.gguf) | 6044.17 MB | | Q3_K_S | [ContextualAI-archangel_sft-kto_llama13b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q3_K_S.gguf) | 5396.82 MB | | Q3_K_L | [ContextualAI-archangel_sft-kto_llama13b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q3_K_L.gguf) | 6608.54 MB | | Q4_K_M | [ContextualAI-archangel_sft-kto_llama13b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q4_K_M.gguf) | 7501.56 MB | | Q5_K_S | [ContextualAI-archangel_sft-kto_llama13b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q5_K_S.gguf) | 8556.64 MB | | Q5_K_M | [ContextualAI-archangel_sft-kto_llama13b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-Q5_K_M.gguf) | 8802.34 MB | | IQ4_XS | [ContextualAI-archangel_sft-kto_llama13b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/ContextualAI-archangel_sft-kto_llama13b-GGUF/blob/main/ContextualAI-archangel_sft-kto_llama13b-IQ4_XS.gguf) | 6694.33 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

Mercuri/MrsNneelijah

Mercuri

Entry not found

darfitos12/fweah

darfitos12

Entry not found

mradermacher/Palworld-SME-13b-GGUF

mradermacher

--- base_model: ericpolewski/Palworld-SME-13b language: - en library_name: transformers license: cc-by-sa-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ericpolewski/Palworld-SME-13b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Palworld-SME-13b-GGUF/resolve/main/Palworld-SME-13b.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

SiRoZaRuPa/wav2vec2-base-kanji-unigram-RS-s-1101

SiRoZaRuPa

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

visdata/gptnoise48

visdata

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]