Text Generation
MLX
Safetensors
English
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
dvilasuero/DistilabelBeagle14-7B
beowolx/CodeNinja-1.0-OpenChat-7B
WizardLM/WizardMath-7B-V1.1
Maths
Code
Python
conversational
Instructions to use mlx-community/Pearl-3x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Pearl-3x7B 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("mlx-community/Pearl-3x7B") 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 mlx-community/Pearl-3x7B with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Pearl-3x7B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Pearl-3x7B" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Pearl-3x7B", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Create README.md
Browse files
README.md
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---
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language:
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- en
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base_model: louisbrulenaudet/Pearl-3x7B
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library_name: mlx
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tags:
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- moe
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- frankenmoe
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- merge
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- mergekit
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- lazymergekit
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- dvilasuero/DistilabelBeagle14-7B
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- beowolx/CodeNinja-1.0-OpenChat-7B
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- WizardLM/WizardMath-7B-V1.1
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- Maths
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- Code
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- Python
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pipeline_tag: text-generation
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license: apache-2.0
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---
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<center><img src='https://i.imgur.com/dU9dUh0.png' width='500px'></center>
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# mlx-community/Pearl-3x7B
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This model was converted to MLX format from [`louisbrulenaudet/Pearl-3x7B`]() using mlx-vlm version **0.16.1**.
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Refer to the [original model card](louisbrulenaudet/Pearl-3x7B) for more details on the model.
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## Use with mlx
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```bash
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pip install -U mlx-vlm
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python -m mlx_vlm.generate --model mlx-community/Pearl-3x7B --max-tokens 100 --temp 0.0
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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("mlx-community/Pearl-3x7B")
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response = generate(model, tokenizer, prompt="hello", verbose=True)
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```
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## Citing & Authors
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If you use this code in your research, please use the following BibTeX entry.
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```BibTeX
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@misc{louisbrulenaudet2024,
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author = {Louis Brulé Naudet},
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title = {Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science},
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year = {2024}
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howpublished = {\url{https://huggingface.co/mlx-community/Pearl-3x7B}},
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}
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```
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## Feedback
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If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
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