johnjadensmith112/Mistral-Small-24B-Instruct-2501-reasoning-Q4-mlx
The Model johnjadensmith112/Mistral-Small-24B-Instruct-2501-reasoning-Q4-mlx was converted to MLX format from yentinglin/Mistral-Small-24B-Instruct-2501-reasoning using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("johnjadensmith112/Mistral-Small-24B-Instruct-2501-reasoning-Q4-mlx")
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 johnjadensmith112/Mistral-Small-24B-Instruct-2501-reasoning-Q4-mlx
Base model
mistralai/Mistral-Small-24B-Base-2501Datasets used to train johnjadensmith112/Mistral-Small-24B-Instruct-2501-reasoning-Q4-mlx
Evaluation results
- pass@1 on MATH-500yentinglin/zhtw-reasoning-eval-leaderboard0.950
- pass@1 on AIME 2025yentinglin/zhtw-reasoning-eval-leaderboard0.533
- pass@1 on AIME 2025yentinglin/zhtw-reasoning-eval-leaderboard0.667
- pass@1 on GPQA Diamondyentinglin/zhtw-reasoning-eval-leaderboard0.620