Instructions to use tussiiiii/Qwen3-4B-AgentBench-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tussiiiii/Qwen3-4B-AgentBench-Merged with Transformers:

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

pipe = pipeline("text-generation", model="tussiiiii/Qwen3-4B-AgentBench-Merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tussiiiii/Qwen3-4B-AgentBench-Merged")
model = AutoModelForCausalLM.from_pretrained("tussiiiii/Qwen3-4B-AgentBench-Merged")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tussiiiii/Qwen3-4B-AgentBench-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tussiiiii/Qwen3-4B-AgentBench-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tussiiiii/Qwen3-4B-AgentBench-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tussiiiii/Qwen3-4B-AgentBench-Merged

SGLang

How to use tussiiiii/Qwen3-4B-AgentBench-Merged 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 "tussiiiii/Qwen3-4B-AgentBench-Merged" \
    --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": "tussiiiii/Qwen3-4B-AgentBench-Merged",
		"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 "tussiiiii/Qwen3-4B-AgentBench-Merged" \
        --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": "tussiiiii/Qwen3-4B-AgentBench-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tussiiiii/Qwen3-4B-AgentBench-Merged with Docker Model Runner:
```
docker model run hf.co/tussiiiii/Qwen3-4B-AgentBench-Merged
```

Qwen3-4B-AgentBench-Merged

This repository provides a merged model fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using LoRA + Unsloth, where the LoRA adapter has been merged into the base model weights.

This repository contains full merged model weights. The model can be loaded directly without requiring a separate adapter.

Training Objective

This model is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn environment observation, action selection, tool use, and recovery from errors.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: LoRA (merged into base model)
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-06
LoRA: r=64, alpha=128

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_id/your-repo"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

Data Sources & Attribution

Training data: tussiiiii/agentbench_sft_mix_alfworld_dbbench_v1

The training dataset above is a mixed dataset created by concatenating and shuffling:

u-10bei/sft_alfworld_trajectory_dataset_v5
u-10bei/dbbench_sft_dataset_react_v4

We sincerely thank the original dataset authors and contributors for making these resources available. Please comply with the licenses and terms of the original datasets and the base model.

Sources & Terms (IMPORTANT)

Mixed dataset: tussiiiii/agentbench_sft_mix_alfworld_dbbench_v1
Source datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4

Compliance: Users must comply with the dataset licenses and the base model's original terms of use.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for tussiiiii/Qwen3-4B-AgentBench-Merged

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(1816)

this model

tussiiiii
/

Qwen3-4B-AgentBench-Merged