51 lines
1.4 KiB
Markdown
51 lines
1.4 KiB
Markdown
---
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license: llama3.1
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datasets:
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- OpenCoder-LLM/opc-sft-stage1
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- OpenCoder-LLM/opc-sft-stage2
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- microsoft/orca-agentinstruct-1M-v1
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- microsoft/orca-math-word-problems-200k
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- NousResearch/hermes-function-calling-v1
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- AI-MO/NuminaMath-CoT
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- AI-MO/NuminaMath-TIR
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- allenai/tulu-3-sft-mixture
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- cognitivecomputations/dolphin-coder
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- HuggingFaceTB/smoltalk
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- cognitivecomputations/samantha-data
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- m-a-p/CodeFeedback-Filtered-Instruction
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- m-a-p/Code-Feedback
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language:
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- en
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base_model: cognitivecomputations/Dolphin3.0-Llama3.1-8B
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tags:
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- mlx
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---
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# mlx-community/Dolphin3.0-Llama3.1-8B-bf16
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The Model [mlx-community/Dolphin3.0-Llama3.1-8B-bf16](https://huggingface.co/mlx-community/Dolphin3.0-Llama3.1-8B-bf16) was
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converted to MLX format from [cognitivecomputations/Dolphin3.0-Llama3.1-8B](https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.1-8B)
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using mlx-lm version **0.20.5**.
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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/Dolphin3.0-Llama3.1-8B-bf16")
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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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