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xfivetide/VibeThinker-3B-hipfire
VibeThinker-3B-hipfire is a text generation model from xfivetide. Use it when you need the model to write or continue text. The card lists the license as mit.
FWHT-rotated quantizations of WeiboAI/VibeThinker-3B for hipfire, a Rust-native inference engine for AMD RDNA GPUs.
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Updated Jun 22, 2026
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From the Hugging Face model README
FWHT-rotated quantizations of WeiboAI/VibeThinker-3B for hipfire, a Rust-native inference engine for AMD RDNA GPUs.
| File | Format | Size | Quality |
|---|---|---|---|
vibethinker-3b.mq4.hfq | MQ4G256 (4-bit FWHT) | 1.8 GB | good |
vibethinker-3b.mq6.hfq | MQ6G256 (6-bit FWHT) | 2.4 GB | better |
MQ4 is the default pick — fastest decode, smallest size.
MQ6 is worth it if you have extra VRAM budget and want tighter quality.
# pull and run (hipfire auto-selects the MQ4 file)
hipfire run xfivetide/VibeThinker-3B-hipfire "Your prompt here"
# or pick a specific quant explicitly
hipfire run xfivetide/VibeThinker-3B-hipfire/vibethinker-3b.mq6.hfq "Your prompt here"
| Format | Size | Decode speed |
|---|---|---|
| BF16 safetensors (original) | 5.8 GB | ~8 tok/s |
| MQ4 (this repo) | 1.8 GB | ~36–90 tok/s |
| MQ6 (this repo) | 2.4 GB | ~25–60 tok/s |
Speed varies with context length (attention cost grows with KV-cache size).
hipfire-quantize \
--input WeiboAI/VibeThinker-3B \
--output vibethinker-3b.mq4.hfq \
--format mq4 \
--arch-id 7
hipfire-quantize \
--input WeiboAI/VibeThinker-3B \
--output vibethinker-3b.mq6.hfq \
--format mq6 \
--arch-id 7
Note:
--arch-id 7is required. The quantizer auto-detectsqwen2as id=1 (llama), which silently drops Q/K/V attention biases at load time.
Both formats use MagnumQuant (MQ) with FWHT rotation (Walsh-Hadamard Transform, group size 256). The FWHT equalizes weight magnitudes across groups before quantization, reducing per-group error vs plain INT4/INT6.
arch_id=7 routes to hipfire-arch-qwen2, which correctly loads Qwen2's Q/K/V attention biasesVibeThinker-3B is a 3B-parameter reasoning model fine-tuned on math, competitive programming, and STEM tasks. It generates <think>…</think> reasoning traces before answering. hipfire's CLI strips them by default; raw JSONL clients see the full stream.
See WeiboAI/VibeThinker-3B for benchmarks and full details (MIT license).