---
base_model:
- hotdogs/Qwen3.8-27B-abliterated
datasets:
- hotdogs/code-analysis-sft-qwen38-v2
library_name: transformers
model_type: qwen3_5
pipeline_tag: text-generation
tags:
- qwen3
- code-review
- code-analysis
- lora
- sft
- abliterated
- multi-token-prediction
- reasoning
- mtp
license: mit
---

# Qwen3.8-27B Code Analysis Preview (v2)

A **code-analysis / code-review** fine-tune of [`hotdogs/Qwen3.8-27B-abliterated`](https://huggingface.co/hotdogs/Qwen3.8-27B-abliterated), trained on the **v2 dataset** that fixes the *template-collapse* problem of v1.

Given a snippet of code, it produces a **structured, multi-paragraph review** — real bugs found, line-level reasoning, severity, and a concrete fix in a code block. It is a **reasoning model**: it thinks first (separated into `reasoning_content` when served) and then answers.

> **v1 → v2:** v1 was trained on a synthetic placeholder dataset (15 unique code bodies, 29–44 char answers like `## Review\n\nFound N issue(s) in L lines.`). The model faithfully reproduced the template — it answered *"No bugs found. Code is clean."* and missed real bugs. **v2** was retrained on 21,009 **real** code+bug+answer rows across 5 languages with 550–880 char detailed answers. The model now actually *finds* the bugs.

## Highlights

- ✅ **Finds real bugs** — off-by-one, missing cache-hit, `fetch` not checking `res.ok`, async races, etc.
- ✅ **Generalizes** — correctly analyzes bug types *not* in the training archetypes (base model supplies the code knowledge; the LoRA supplies the review structure)
- ✅ **No hallucination** on clean code — says *"correct, no bugs"* instead of inventing problems
- ✅ **Reasoning separated** — internal monologue goes to `reasoning_content`, user sees only the answer
- ✅ **MTP preserved** — 15 multi-token-prediction tensors (`mtp.*` / `blk.64.nextn.*`) kept for speculative decoding

## How it was made

| Step | Detail |
|---|---|
| Base | `hotdogs/Qwen3.8-27B-abliterated` (abliterated, ~27B) |
| Method | Unsloth LoRA, **r=32**, 233M trainable params (0.85%) |
| Dataset | [`hotdogs/code-analysis-sft-qwen38-v2`](https://huggingface.co/datasets/hotdogs/code-analysis-sft-qwen38-v2) — 21,009 train / 1,900 valid |
| Languages | Python, JavaScript, Go, Rust, C |
| Answer style | 550–880 chars, line numbers, severity, fix code block |
| Sequence | max 2048 tokens, bf16, 5× RTX 3090 |
| Early stop | step 400 / 1313 (epoch ~0.30, loss ~0.0003) — stopped before the 15 archetypes were memorized to death |
| Merge | `merge_and_unload`, MTP 15 tensors recovered, no triple-nest |

## Smoke test (v2)

| Case | Result |
|---|---|
| Off-by-one (in-archetype) | 🟢 Found it + fix + docstring note |
| Async race (unseen) | 🟢 "no cache-hit fast path" + concurrency |
| Clean code (hallucination test) | 🟢 "correct, no bugs" + minor float/bool note |

## Usage (transformers)

```python
from transformers import AutoModelForImageTextToText, AutoTokenizer
import torch

MODEL = "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview"
tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL, torch_dtype=torch.bfloat16,
    device_map="auto", trust_remote_code=True, attn_implementation="sdpa")
model.eval()

def review(code, max_new=600):
    text = tok.apply_chat_template(
        [{"role": "user", "content": "Review this code and report any bugs you find.\n\n```python\n" + code + "\n```"}],
        tokenize=False, add_generation_prompt=True)
    inputs = tok(text, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(input_ids=inputs["input_ids"],
                             attention_mask=inputs["attention_mask"],
                             max_new_tokens=max_new, do_sample=False,
                             repetition_penalty=1.05)
    new = out[0][inputs["input_ids"].shape[1]:]
    return tok.decode(new, skip_special_tokens=True)
```

## Usage (GGUF)

See the GGUF repo → [`hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview-mtp-GGUF`](https://huggingface.co/hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview-mtp-GGUF)

## Files

12 safetensors shards (~52 GB, bf16) + tokenizer, processor, config, chat template, generation config. 1,199 tensors incl. 15 MTP.

## License

MIT (inherits the abliterated base).