Instructions to use happybrian/fast-brain-translate-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use happybrian/fast-brain-translate-adapter with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir fast-brain-translate-adapter happybrian/fast-brain-translate-adapter
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Fast-Brain translate Cortex Adapter (LoRA)
translate 专家皮层 LoRA 适配器(~20MB),需配合基底 happybrian/fast-brain-base 使用。
from huggingface_hub import snapshot_download
from mlx_lm import load, generate
base = snapshot_download("happybrian/fast-brain-base") # 皮层基底
adapter = snapshot_download("happybrian/fast-brain-translate-adapter") # 本皮层
model, tokenizer = load(base, adapter_path=adapter)
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "..."}], tokenize=False, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=256))
- 训练:LoRA r=16, 800 步, 蒸馏数据 ~450-650 条(教师 Qwen3-8B-4bit)
- 评测:中英双向人工抽检通过
- 硬件:Apple M5 24GB, mlx-lm
Hardware compatibility
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Model tree for happybrian/fast-brain-translate-adapter
Base model
Qwen/Qwen2.5-1.5B Finetuned
Qwen/Qwen2.5-1.5B-Instruct Finetuned
happybrian/fast-brain-base