Instructions to use hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview")
model = AutoModelForMultimodalLM.from_pretrained("hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview

SGLang

How to use hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview with Docker Model Runner:
```
docker model run hf.co/hotdogs/Qwen3.8-27B-abliterated-code-analysis-preview
```

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

A code-analysis / code-review fine-tune of 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` — 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)

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)