Instructions to use codingmavin/Fino1-8B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use codingmavin/Fino1-8B-mlx-8Bit 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("codingmavin/Fino1-8B-mlx-8Bit") 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
- LM Studio
- Pi new
How to use codingmavin/Fino1-8B-mlx-8Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "codingmavin/Fino1-8B-mlx-8Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "codingmavin/Fino1-8B-mlx-8Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use codingmavin/Fino1-8B-mlx-8Bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "codingmavin/Fino1-8B-mlx-8Bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default codingmavin/Fino1-8B-mlx-8Bit
Run Hermes
hermes
- MLX LM
How to use codingmavin/Fino1-8B-mlx-8Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "codingmavin/Fino1-8B-mlx-8Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "codingmavin/Fino1-8B-mlx-8Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codingmavin/Fino1-8B-mlx-8Bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: llama3.1
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datasets:
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- TheFinAI/Fino1_Reasoning_Path_FinQA
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language:
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- en
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base_model: TheFinAI/Fino1-8B
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pipeline_tag: text-generation
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tags:
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- mlx
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---
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# codingmavin/Fino1-8B-mlx-8Bit
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The Model [codingmavin/Fino1-8B-mlx-8Bit](https://huggingface.co/codingmavin/Fino1-8B-mlx-8Bit) was converted to MLX format from [TheFinAI/Fino1-8B](https://huggingface.co/TheFinAI/Fino1-8B) using mlx-lm version **0.22.1**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("codingmavin/Fino1-8B-mlx-8Bit")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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