Instructions to use code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only with Transformers:

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

pipe = pipeline("text-generation", model="code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only")
model = AutoModelForCausalLM.from_pretrained("code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only

SGLang

How to use code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only 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 "code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only" \
    --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": "code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only",
		"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 "code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only" \
        --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": "code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only with Docker Model Runner:
```
docker model run hf.co/code-critic-model/Qwen3-8B-Critic-SFT-Qwen-only
```

Qwen3-8B-Critic-SFT-Qwen-only

An 8B critic from Steer, Don't Solve: Training Small Critic Models for Large Code Agents, trained on critiques of Qwen3-Next-80B-A3B trajectories only. It is one arm of the training-corpus ablation (Table 3). The main 8B critic, trained on CWM plus Qwen3-Next trajectories, is Qwen3-8B-Critic-SFT.

The critic reads a coding agent's trajectory every k steps and returns a structured critique: detected error categories, evidence, a recovery action, task status, and one line of overall guidance. It does not write the patch.

All released models and datasets are listed on the organization page. Code and configs are in the critic-training repository.

Where it appears in the paper

Paper location	Row label
Table 3, corpus ablation	`8B, Qwen-only`

Original run name: qwen3-8b-full-sft-prm-r2egym-swebench-instructions-k5-qwen-only-opus-distill-32k-lr5e6-multiturn.

Training data

code-critic-model/critic-sft-qwen-only, 1,915 examples.

Tasks: 483 R2E-Gym instances from matplotlib, moto, and sympy, disjoint from SWE-bench Verified.
Agent that produced the trajectories: Qwen3-Next-80B-A3B-Instruct, 483 trajectories.
Teacher: Claude Opus 4.6, queried every 5 agent steps with the high-level prompt.

This corpus is the Qwen half of critic-sft-cwm-qwen. It is smaller than the CWM half because Qwen3-Next finishes tasks in fewer steps, so fewer critique points are collected per trajectory.

Training setup

Identical to Qwen3-8B-Critic-SFT apart from the data. Full-parameter SFT with LLaMA-Factory, config finetuning/qwen3_8b_critic_full_sft_l40s_train_multiturn_resumable.yaml.

Setting	Value
Base model	Qwen/Qwen3-8B
Chat template	`qwen3_nothink`
Sequence length	32,768 tokens
Loss	final critique turn only (`mask_history: true`)
Hardware	8 x L40S, effective batch 8
Optimizer	AdamW, lr 5e-6, cosine, warmup ratio 0.1
Epochs	3
Precision	bf16

The trainer saved this run's weights in fp32. The checkpoint here was cast to bf16 before upload, which is the precision it was served in for every result in the paper.

Results

Resolve rate on SWE-bench Verified, from Table 3 of the paper.

Coding agent	No critic	+ this critic	+ Qwen3-8B-Critic-SFT (CWM + Qwen data)
Qwen3-Next-80B-A3B	20.0	26.2	25.2
Qwen3-32B	8.8	10.6	13.8

Training only on Qwen3-Next trajectories gives the best result on Qwen3-Next itself and the weakest on Qwen3-32B. The mixed corpus is the better default across agents.

How to use

Same serving and launch procedure as Qwen3-8B-Critic-SFT: serve with vLLM in bf16 and pass the served name to scripts/run_critic_max150.sh with --prm. The served name must have an entry in mini-swe-agent/configs/litellm_model_registry.json; add one for this model if you use a new name.

Citation

@misc{gandhi2026steerdontsolvetraining,
  title={Steer, Don't Solve: Training Small Critic Models for Large Code Agents},
  author={Shubham Gandhi and Yiqing Xie and Atharva Naik and Ruichen Zhu and Carolyn Rose},
  year={2026},
  eprint={2606.21811},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2606.21811}
}