nhe-ai/QwQ-R1-Distill-7B-CoT-mlx-4Bit
The Model nhe-ai/QwQ-R1-Distill-7B-CoT-mlx-4Bit was converted to MLX format from prithivMLmods/QwQ-R1-Distill-7B-CoT using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nhe-ai/QwQ-R1-Distill-7B-CoT-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for nhe-ai/QwQ-R1-Distill-7B-CoT-mlx-4Bit
Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Finetuned
prithivMLmods/QwQ-R1-Distill-7B-CoT
Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard35.000
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard20.950
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard27.190
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.820
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.500
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard20.050