This repository contains bottlecapai/ThinkingCap-Qwen3.8-27B converted to MLX format with a mixed-precision quantization recipe, using mlx-vlm 0.6.17.
Quantization recipe
| Module | Precision |
|---|---|
MLP gate_proj / up_proj / down_proj (64 layers) | mxfp8 (group_size=32, bits=8) |
Full attention q_proj / k_proj / v_proj / o_proj (16 layers) | mxfp8 (group_size=32, bits=8) |
Linear (GDN) attention in_proj_* / out_proj (48 layers) | mxfp8 (group_size=32, bits=8) |
Token embeddings (embed_tokens) | bfloat16 |
Output head (lm_head) | bfloat16 |
| MTP head | bfloat16 |
| Vision tower | bfloat16 |
- Effective size: ~32 GB (9.2 bits per weight), base model is ~54 GB in bfloat16.
- Quantized modules: mxfp8 (group_size=32, bits=8); bfloat16 modules are stored as-is. Per-module precision is detected from the presence of
.scalestensors.
MTP
The native MTP head is merged into this checkpoint as language_model.mtp.* tensors (15 tensors, bfloat16, norms in the MLX +1 convention), stored in mtp.safetensors and referenced from model.safetensors.index.json — it is not a separate drafter model. Use it for speculative decoding (--draft-kind mtp in mlx-vlm) or ignore it; base inference is unaffected.
Use with mlx-vlm
pip install mlx-vlm
import mlx_vlm
model, processor = mlx_vlm.load("airagrp/ThinkingCap-Qwen3.8-27B-mlx-mxfp8")
response, _ = mlx_vlm.generate(
model,
processor,
prompts="In one sentence, what is MLX?",
max_tokens=64,
)
print(response)
mlx_vlm.generate --model airagrp/ThinkingCap-Qwen3.8-27B-mlx-mxfp8 --prompt "In one sentence, what is MLX?" --max-tokens 64
Use with MLX directly
Load with the standard MLX safetensors layout; quantized weights use mxfp8 (group_size=32, bits=8).
Citations / license
This checkpoint derives from bottlecapai/ThinkingCap-Qwen3.8-27B (a finetune); the finetune weights are licensed under polyform-small-business-1.0.0. See LICENSE in this repo. Refer to the original model card for architecture details, benchmarks, and usage guidelines.
Quality benchmarks
Perplexity and KL divergence on the WikiText-2 raw test split (297,193 tokens, 2048-token windows, fresh KV cache per window), measured against the bf16 reference (ThinkingCap-Qwen3.8-27B).
| Model | Size (GiB) | PPL (lower = better) | ΔPPL vs bf16 | KLD vs bf16 (nats/token, lower = better) |
|---|---|---|---|---|
ThinkingCap-Qwen3.8-27B (bf16 reference) | 51.75 | 7.008 | — | — |
ThinkingCap-Qwen3.8-27B-mlx-nvfp4 | 22.31 | 7.075 | +0.068 | 0.0427 |
ThinkingCap-Qwen3.8-27B-mlx-mxfp8 | 29.78 | 6.962 | -0.046 | 0.0077 |
- KLD = mean per-token
D_KL(p_bf16 ‖ p_model)over the full vocabulary — how much the quantized model's next-token distribution drifts from bf16. - Data:
wikitext-2-raw-v1test split, SHA-256696cca6b65a171b0…; tokenizer:ThinkingCap-Qwen3.8-27B; mlx-vlm 0.6.17. - Benchmarked 2026-09-24 with
benchmark_ppl_kld.py(2048-token streaming windows, all positions except the first of each window scored).
Plots
