base_model: rombodawg/rombos_Llama3-8B-Instruct-Replete-Adapted
datasets:
- Replete-AI/code_bagel_hermes-2.5
- Replete-AI/code_bagel
- Replete-AI/OpenHermes-2.5-Uncensored
- teknium/OpenHermes-2.5
- layoric/tiny-codes-alpaca
- glaiveai/glaive-code-assistant-v3
- ajibawa-2023/Code-290k-ShareGPT
- TIGER-Lab/MathInstruct
- chargoddard/commitpack-ft-instruct-rated
- iamturun/code_instructions_120k_alpaca
- ise-uiuc/Magicoder-Evol-Instruct-110K
- cognitivecomputations/dolphin-coder
- nickrosh/Evol-Instruct-Code-80k-v1
- coseal/CodeUltraFeedback_binarized
- glaiveai/glaive-function-calling-v2
- CyberNative/Code_Vulnerability_Security_DPO
- jondurbin/airoboros-2.2
- camel-ai
- lmsys/lmsys-chat-1m
- CollectiveCognition/chats-data-2023-09-22
- CoT-Alpaca-GPT4
- WizardLM/WizardLM_evol_instruct_70k
- WizardLM/WizardLM_evol_instruct_V2_196k
- teknium/GPT4-LLM-Cleaned
- GPTeacher
- OpenGPT
- meta-math/MetaMathQA
- Open-Orca/SlimOrca
- garage-bAInd/Open-Platypus
- anon8231489123/ShareGPT_Vicuna_unfiltered
- Unnatural-Instructions-GPT4
language:
- en
library_name: transformers
license: other
license_link: https://llama.meta.com/llama3/license/
license_name: llama-3
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
About
static quants of https://huggingface.co/rombodawg/rombos_Llama3-8B-Instruct-Replete-Adapted
weighted/imatrix quants are available at https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 | Q2_K | 3.3 | |
GGUF | IQ3_XS | 3.6 | |
GGUF | Q3_K_S | 3.8 | |
GGUF | IQ3_S | 3.8 | beats Q3_K* |
GGUF | IQ3_M | 3.9 | |
GGUF | Q3_K_M | 4.1 | lower quality |
GGUF | Q3_K_L | 4.4 | |
GGUF | IQ4_XS | 4.6 | |
GGUF | Q4_K_S | 4.8 | fast, recommended |
GGUF | Q4_K_M | 5.0 | fast, recommended |
GGUF | Q5_K_S | 5.7 | |
GGUF | Q5_K_M | 5.8 | |
GGUF | Q6_K | 6.7 | very good quality |
GGUF | Q8_0 | 8.6 | fast, best quality |
GGUF | f16 | 16.2 | 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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @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.