Instructions to use 3thn/dolphin-2.9-llama3-70b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use 3thn/dolphin-2.9-llama3-70b-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir dolphin-2.9-llama3-70b-4bit 3thn/dolphin-2.9-llama3-70b-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
metadata
language:
- en
license: llama3
tags:
- mlx
datasets:
- 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
3thn/dolphin-2.9-llama3-70b-4bit
This model was converted to MLX format from cognitivecomputations/dolphin-2.9-llama3-70b using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("3thn/dolphin-2.9-llama3-70b-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)