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--- |
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license: llama3.1 |
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base_model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b |
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tags: |
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- generated_from_trainer |
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- mlx |
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datasets: |
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- cognitivecomputations/Dolphin-2.9 |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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- cognitivecomputations/dolphin-coder |
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- cognitivecomputations/samantha-data |
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- microsoft/orca-math-word-problems-200k |
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- mlabonne/FineTome-100k |
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- arcee/agent_data |
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- PawanKrd/math-gpt-4o-200k |
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- cognitivecomputations/SystemChat-2.0 |
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--- |
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# mlx-community/dolphin-2.9.4-llama3.1-8b-4bit |
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The Model [mlx-community/dolphin-2.9.4-llama3.1-8b-4bit](https://huggingface.co/mlx-community/dolphin-2.9.4-llama3.1-8b-4bit) was converted to MLX format from [cognitivecomputations/dolphin-2.9.4-llama3.1-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b) using mlx-lm version **0.19.1**. |
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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("mlx-community/dolphin-2.9.4-llama3.1-8b-4bit") |
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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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