Instructions to use introvoyz041/RetroDFM-R-8B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use introvoyz041/RetroDFM-R-8B-mlx-4Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("introvoyz041/RetroDFM-R-8B-mlx-4Bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use introvoyz041/RetroDFM-R-8B-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "introvoyz041/RetroDFM-R-8B-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "introvoyz041/RetroDFM-R-8B-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "introvoyz041/RetroDFM-R-8B-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| license: gpl-3.0 | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| tags: | |
| - chemistry | |
| - mlx | |
| - mlx-my-repo | |
| base_model: OpenDFM/RetroDFM-R-8B | |
| # introvoyz041/RetroDFM-R-8B-mlx-4Bit | |
| The Model [introvoyz041/RetroDFM-R-8B-mlx-4Bit](https://huggingface.co/introvoyz041/RetroDFM-R-8B-mlx-4Bit) was converted to MLX format from [OpenDFM/RetroDFM-R-8B](https://huggingface.co/OpenDFM/RetroDFM-R-8B) using mlx-lm version **0.28.3**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("introvoyz041/RetroDFM-R-8B-mlx-4Bit") | |
| prompt="hello" | |
| if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |