Instructions to use mlx-community/CodelLama7B-inst-dpo-7k-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodelLama7B-inst-dpo-7k-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodelLama7B-inst-dpo-7k-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use mlx-community/CodelLama7B-inst-dpo-7k-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/CodelLama7B-inst-dpo-7k-mlx" --prompt "Once upon a time"
mlx-community/CodelLama7B-inst-dpo-7k-mlx
This model is a finetuned MLX version from mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX.
Refer to the original model card for more details on the model.
Using argilla 7k DPO dataset
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/CodelLama7B-inst-dpo-7k-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
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Model size
1B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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