shreyan35/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-mlx-8Bit
This model is a finetuned and optimised quant of the original model. Boost in speed (+~35%) and optimised for macs
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("shreyan35/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-mlx-8Bit")
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 size
35B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
Model tree for shreyan35/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-mlx-8Bit
Base model
Qwen/Qwen3.6-35B-A3BDatasets used to train shreyan35/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-mlx-8Bit
Evaluation results
- exact_match, custom-extract, limited sample on MMLU-Protest set self-reported75.710