dolphin-2.9-llama3-8b-AWQ/README.md

license, base_model, library_name, tags, pipeline_tag, inference, quantized_by, datasets

license	base_model	library_name	tags	pipeline_tag	inference	quantized_by	datasets
other	meta-llama/Meta-Llama-3-8B	transformers	4-bit AWQ text-generation autotrain_compatible endpoints_compatible generated_from_trainer	text-generation	false	Suparious	cognitivecomputations/Dolphin-2.9 teknium/OpenHermes-2.5 m-a-p/CodeFeedback-Filtered-Instruction cognitivecomputations/dolphin-coder cognitivecomputations/samantha-data HuggingFaceH4/ultrachat_200k microsoft/orca-math-word-problems-200k abacusai/SystemChat-1.1 Locutusque/function-calling-chatml internlm/Agent-FLAN

cognitivecomputations/dolphin-2.9-llama3-8b AWQ

Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations

Discord: https://discord.gg/8fbBeC7ZGx

My appreciation for the sponsors of Dolphin 2.9:

• Crusoe Cloud - provided excellent on-demand 10xL40S node

This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT

The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.

It took 2.5 days on 8x L40S provided by Crusoe Cloud

This model was trained FFT on all parameters, using ChatML prompt template format.

example:

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant