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
File size: 922 Bytes
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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](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-70b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("3thn/dolphin-2.9-llama3-70b-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
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