Delta-Vector
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README.md
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tags:
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### exl2 quant (measurement.json in main branch)
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---
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### check revisions for quants
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---
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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```yaml
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base_model: Dans-DiscountModels/Mistral-NeMo-Minitron-8B-Base-ChatML
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model_type: AutoModelForCausalLM
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strict: false
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datasets:
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- path:
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type: sharegpt
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conversation: chatml
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- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
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fsdp_config:
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special_tokens:
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pad_token: <pad>
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```
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</details><br>
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# workspace/data/8b-nemo-fft-out
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This model is a fine-tuned version of [Dans-DiscountModels/Mistral-NeMo-Minitron-8B-Base-ChatML](https://huggingface.co/Dans-DiscountModels/Mistral-NeMo-Minitron-8B-Base-ChatML) on the None dataset.
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## Model description
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 10
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 40
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- total_eval_batch_size: 20
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 4
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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---
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License: agpl-3.0
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: nvidia/Mistral-NeMo-Minitron-8B-Base
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Tags:
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- Chat
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license: agpl-3.0
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datasets:
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
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- anthracite-org/nopm_claude_writing_fixed
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo_opus_misc_240827
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- anthracite-org/kalo_misc_part2
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tags:
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- chat
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language:
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- en
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base_model:
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- nvidia/Mistral-NeMo-Minitron-8B-Base
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---
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This is the fully cooked, 4 epoch version of [Tor-8B](), this is an experimental version, despite being trained for 4 epochs, the model feels fresh and new and is not overfit, This model aims to have generally good prose and writing while not falling into claude-isms, it follows the *actions* "dialogue" format heavily.
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# These are EXL2 quantizations for Darkens-8B, for the weights, go [here](https://huggingface.co/Delta-Vector/Darkens-8B), Check revisions for quants, Main repo contains measurement.
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# Quants
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GGUF: https://huggingface.co/Delta-Vector/Darkens-8B-GGUF
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EXL2: https://huggingface.co/Delta-Vector/Darkens-8B-EXL2
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## Prompting
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Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
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```py
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"""<|im_start|>system
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system prompt<|im_end|>
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<|im_start|>user
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Hi there!<|im_end|>
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<|im_start|>assistant
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Nice to meet you!<|im_end|>
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<|im_start|>user
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Can I ask a question?<|im_end|>
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<|im_start|>assistant
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"""
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```
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## System Prompting
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I would highly recommend using Sao10k's Euryale System prompt, But the "Roleplay Simple" system prompt provided within SillyTavern will work aswell.
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```
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Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.
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<Guidelines>
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• Maintain the character persona but allow it to evolve with the story.
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• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.
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• All types of outputs are encouraged; respond accordingly to the narrative.
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• Include dialogues, actions, and thoughts in each response.
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• Utilize all five senses to describe scenarios within {{char}}'s dialogue.
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• Use emotional symbols such as "!" and "~" in appropriate contexts.
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• Incorporate onomatopoeia when suitable.
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• Allow time for {{user}} to respond with their own input, respecting their agency.
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• Act as secondary characters and NPCs as needed, and remove them when appropriate.
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• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.
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</Guidelines>
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<Forbidden>
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• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.
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• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.
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• Repetitive and monotonous outputs.
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• Positivity bias in your replies.
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• Being overly extreme or NSFW when the narrative context is inappropriate.
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</Forbidden>
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Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.
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```
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## Axolotl config
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<details><summary>See axolotl config</summary>
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Axolotl version: `0.4.1`
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```yaml
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base_model: Dans-DiscountModels/Mistral-NeMo-Minitron-8B-Base-ChatML
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model_type: AutoModelForCausalLM
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strict: false
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datasets:
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- path: PRIVATE CLAUDE LOG FILTER
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type: sharegpt
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conversation: chatml
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- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
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fsdp_config:
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special_tokens:
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pad_token: <pad>
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```
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</details><br>
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## Credits
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Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [Kalomaze](https://huggingface.co/kalomaze), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) (But not Alpin.)
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## Training
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The training was done for 4 epochs. I used 10 x [A40s](https://www.nvidia.com/en-us/data-center/a40/) GPUs graciously provided by [Kalomaze](https://huggingface.co/kalomaze) for the full-parameter fine-tuning of the model.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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