72 lines
1.8 KiB
Markdown
72 lines
1.8 KiB
Markdown
---
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library_name: mlx
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-0.6B-MLX-bf16
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tags:
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- mlx
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datasets:
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- DuckyBlender/diego-replies-dataset
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---
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# DuckyBlender/diegogpt-v2-mlx-bf16
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This model [DuckyBlender/diegogpt-v2-mlx-bf16](https://huggingface.co/DuckyBlender/diegogpt-v2-mlx-bf16) is a full fine-tune of [Qwen/Qwen3-0.6B-MLX-bf16](https://huggingface.co/Qwen/Qwen3-0.6B-MLX-bf16), trained on the complete set of public replies from a specific individual.
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Training was conducted using `mlx-lm` version **0.26.0**. It ran for 15 steps with a batch size of 16, completing in a few seconds on a MacBook Pro M1 Pro (8-core CPU, 16GB RAM). Peak memory usage was 8.3GB. The dataset contained 225 low-quality training pairs (240 lines trained total).
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Run with system prompt `/no_think` and the following generation parameters:
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- `--temp 0.7`
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- `--top-p 0.8`
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- `--top-k 20`
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- `--min-p 0`
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Example usage:
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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("DuckyBlender/diegogpt-v2-mlx-bf16")
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prompt = "are you red hat hacker?"
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if tokenizer.chat_template is not None:
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messages = [
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{"role": "user", "content": user_input}
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False, enable_thinking=False)
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else:
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prompt = user_input
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sampler = make_sampler(temp=0.7, top_p=0.8, top_k=20, min_p=0)
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response = mlx_lm.generate(
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model,
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tokenizer,
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prompt=prompt,
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sampler=sampler,
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verbose=True
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)
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```
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Or directly via CLI:
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```bash
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mlx_lm.generate \
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--model "DuckyBlender/diegogpt-v2-mlx-bf16" \
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--temp 0.7 \
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--top-p 0.8 \
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--top-k 20 \
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--min-p 0 \
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--system "/no_think" \
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--prompt "are you red hat hacker?"
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```
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Model uses \~1.25GB RAM during inference.
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