Instructions to use OpenMOSS-Team/Qwen3-8B-ABC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMOSS-Team/Qwen3-8B-ABC with Transformers:

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

pipe = pipeline("text-generation", model="OpenMOSS-Team/Qwen3-8B-ABC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMOSS-Team/Qwen3-8B-ABC")
model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/Qwen3-8B-ABC")
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

vLLM

How to use OpenMOSS-Team/Qwen3-8B-ABC with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OpenMOSS-Team/Qwen3-8B-ABC

SGLang

How to use OpenMOSS-Team/Qwen3-8B-ABC 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 "OpenMOSS-Team/Qwen3-8B-ABC" \
    --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": "OpenMOSS-Team/Qwen3-8B-ABC",
		"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 "OpenMOSS-Team/Qwen3-8B-ABC" \
        --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": "OpenMOSS-Team/Qwen3-8B-ABC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenMOSS-Team/Qwen3-8B-ABC with Docker Model Runner:
```
docker model run hf.co/OpenMOSS-Team/Qwen3-8B-ABC
```

Qwen3-8B-ABC / README.md

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metadata

license: apache-2.0
language:
  - en
  - zh
tags:
  - qwen3
  - qwen
  - causal-lm
  - transformers
  - instruction-tuning
  - sft
  - agents
  - code
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-8B
model_name: Qwen3-8B-ABC

Qwen3-8B-ABC

💻 Code | 📑 Paper | 📝 Blog | 🤗 Data

Qwen3-8B-ABC is a supervised fine-tuned (SFT) variant of Qwen/Qwen3-8B, trained for agentic backend coding and tool-using / instruction-following behaviors.

Model Details

Model name: Qwen3-8B-ABC
Base model: Qwen/Qwen3-8B
Model type: Causal Language Model (decoder-only)
Training method: Agentic Supervised Fine-Tuning (SFT)

Training Data

This model was fine-tuned on nex-agi/agent-sft.

Please refer to the dataset card for detailed documentation, licensing, and usage constraints.

Performance on ABC-Bench

Following the ABC-Bench paper’s evaluation protocol:

Model	Setting	Average Pass@1 (%, 3 attempts)
Qwen3-8B-ABC	w/ SFT	13.9%
Qwen3-8B	w/o SFT	8.3%

Intended Use

Qwen3-8B-ABC is intended for:

Agent-style instruction following for backend development tasks
Code editing / patch generation in real repositories
Command-line oriented debugging and step-by-step problem solving
Research on automated software engineering and agent evaluation

Usage

Transformers (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "OpenMOSS-Team/Qwen3-8B-ABC"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "Write a FastAPI endpoint that returns health status as JSON."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Citation

@misc{yang2026abcbenchbenchmarkingagenticbackend,
      title={ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development}, 
      author={Jie Yang and Honglin Guo and Li Ji and Jiazheng Zhou and Rui Zheng and Zhikai Lei and Shuo Zhang and Zhiheng Xi and Shichun Liu and Yuxin Wang and Bo Wang and Yining Zheng and Tao Gui and Xipeng Qiu},
      year={2026},
      eprint={2601.11077},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2601.11077}, 
}

Acknowledgements

Base model: Qwen/Qwen3-8B
Training dataset: nex-agi/agent-sft