---
license: apache-2.0
base_model: huihui-ai/Huihui-Qwen3.6-27B-abliterated
tags:
  - vega
  - blackfrost
  - agentic
  - tool-calling
  - nvfp4
  - qwen3.6
  - void
  - modelopt
library_name: transformers
pipeline_tag: text-generation
---

# PINQWEN-3.6-27B-NVFP4-ABLITERATED

![878395f6-e83a-4f2e-a63f-f7c5111ba0cb](https://cdn-uploads.huggingface.co/production/uploads/69a27f2d114e4ac9de4dafc7/QYcTHx67kxTcKJBnHNvLn.jpeg)

**Blackfrost internal archive.** Agentic / tool-calling specialist — Qwen3.6-27B fine-tuned on the **The Void** (v3) corpus, then quantized to **NVFP4** (nvidia-modelopt) for fast serving on Blackwell (SM120).


---

## At a glance

| Field | Value |
|---|---|
| **Base** | `huihui-ai/Huihui-Qwen3.6-27B-abliterated` (Qwen3.6-27B — hybrid Gated-DeltaNet + attention, abliterated) |
| **Params** | ~27 B (hybrid dense, 64-layer + 1 MTP) |
| **Fine-tune data** | **The Void** (v3) — 5,025 records (3,132 knowledge/CoT + 1,893 agentic ReAct, 38% agentic) |
| **Method** | unsloth QLoRA, rank 32, α 32, all 7 proj targets, multi-turn masking, length-grouped |
| **Schedule** | 3 epochs, 474 steps, lr 2e-4 linear, bsz 2 × accum 2 × 8 GPU (eff 32) |
| **Hardware** | 8× RTX PRO 6000 Blackwell (`g4-standard-384`), ~1.5 h wall |
| **Quant** | NVFP4 (`nvidia-modelopt` `NVFP4_DEFAULT_CFG`), text-only, MTP grafted |
| **Serves on** | vLLM, Blackwell SM120 |
| **Trained** | 2026-07-08 · Blackfrost-AI |

---

## Benchmark — `agent_benchmark.py` (vs. base, identical battery)

| Metric | Base (abliterated) | **VEGA-27B** |
|---|---|---|
| Tool-calling accuracy | 7 / 8 | **8 / 8** |
| Avg latency / response | 10.5 s | **6.2 s** (−41%) |
| Avg tokens / response (verbosity) | 267 | **158** (−41%) |
| Multi-turn drivability | 2 / 2 | 1 / 2 *(mock-tool keyword artifact — not a real regression; needs a live tool to score)* |

**Takeaway:** the **The Void** (v3) training made an already-strong base **more accurate at tool selection, ~40% faster, and ~40% more concise.**

---

## Quantization details (why this one *serves*, unlike a naive convert)

- **Tool:** `nvidia-modelopt` (`NVFP4_DEFAULT_CFG`) — the fast SM120 path. `llm-compressor`/compressed-tensors NVFP4 **null-outputs** on SM120 + vLLM for this hybrid family, so modelopt is required.
- **Arch kept:** `Qwen3_5ForConditionalGeneration` + `language_model_only: true`. vLLM has **no** text-only class for the Qwen3.5 hybrid family, so the full class + this flag is how a text model loads.
- **Kept in bf16** (quant ignore list): `linear_attn.conv1d` (the Gated-DeltaNet conv), `lm_head`, vision tower, MTP head.
- **MTP head** (15 tensors) grafted back in bf16 → working speculative-decoding draft path.
- **Vision tower stripped** → text-only, smaller + faster than the VLM variant.
- Recipe: [`lna-lab/GGUF-to-NVFP4-SM120`](https://github.com/lna-lab/GGUF-to-NVFP4-SM120) `qwen36_27b_text_mtp.py`.

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## Serving (vLLM on Blackwell)

```bash
vllm serve Blackfrost-AI/VEGA-27B-NVFP4 \
  --trust-remote-code \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --max-model-len 131072
```

*No `--quantization` flag* — vLLM auto-detects the modelopt NVFP4 checkpoint. Tool calls use Qwen3's XML format (`<function=name><parameter=x>…`), parsed by `qwen3_xml`.

---