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| Metric | Value |
|---|---|
| In-memory footprint | ~25 GiB |
| Size on disk | 26.3 GB |
| Average bits per weight | 5.29 |
| Group size | 64 |
| Framework | MLX (Apple Silicon) |
| Source | Qwen/Qwen3.6-35B-A3B (BF16, 71.9 GB) |
1sampler_params = {
2 "temperature": 1.0,
3 "top_p": 0.95,
4 "top_k": 40,
5 "repetition_penalty": 1.1,
6 "max_tokens": 8192,
7}1from mlx_lm import load, generate
2from mlx_lm.sample_utils import make_sampler, make_logits_processors
3
4model, tokenizer = load("baa-ai/Qwen3.6-35B-A3B-RAM-25GB-MLX")
5
6sampler = make_sampler(temp=1.0, top_p=0.95, top_k=40)
7logits_processors = make_logits_processors(repetition_penalty=1.1)
8
9prompt = tokenizer.apply_chat_template(
10 [{"role": "user", "content": "Write a Python function that reverses a string."}],
11 tokenize=False,
12 add_generation_prompt=True,
13)
14
15response = generate(model, tokenizer, prompt=prompt, max_tokens=8192,
16 sampler=sampler, logits_processors=logits_processors)
17print(response)| Variant | Size | Link |
|---|---|---|
| 19 GB | 20.6 GB | baa-ai/Qwen3.6-35B-A3B-RAM-19GB-MLX |
| 25 GB | 26.3 GB | baa-ai/Qwen3.6-35B-A3B-RAM-25GB-MLX |