Instructions to use Minbyul/AgentMercury-Qwen3.5-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minbyul/AgentMercury-Qwen3.5-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Minbyul/AgentMercury-Qwen3.5-4B")
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("Minbyul/AgentMercury-Qwen3.5-4B")
model = AutoModelForMultimodalLM.from_pretrained("Minbyul/AgentMercury-Qwen3.5-4B", 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 Minbyul/AgentMercury-Qwen3.5-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minbyul/AgentMercury-Qwen3.5-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minbyul/AgentMercury-Qwen3.5-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Minbyul/AgentMercury-Qwen3.5-4B

SGLang

How to use Minbyul/AgentMercury-Qwen3.5-4B 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 "Minbyul/AgentMercury-Qwen3.5-4B" \
    --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": "Minbyul/AgentMercury-Qwen3.5-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Minbyul/AgentMercury-Qwen3.5-4B" \
        --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": "Minbyul/AgentMercury-Qwen3.5-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Minbyul/AgentMercury-Qwen3.5-4B with Docker Model Runner:
```
docker model run hf.co/Minbyul/AgentMercury-Qwen3.5-4B
```

AgentMercury-Qwen3.5-4B

AgentMercury-Qwen3.5-4B is a Qwen3.5-4B (multimodal, Qwen3_5ForConditionalGeneration) checkpoint post-trained with agentic reinforcement learning on MCP (Model-Context-Protocol) tool-use environments. The RL objective rewards completing real multi-turn agent tasks (correct tool calls, correct final database/environment state), not just producing text.

This checkpoint is the clean-minimum of the run: the step at which reward peaks while degenerate-generation rate and truncation rate are both exactly 0 — before later steps regress into verbosity / reward-hacking.

Highlights

Base: Qwen3.5-4B (text + vision).
Method: on-policy GRPO, 200-step MCP agentic RL (slime trainer + sglang rollout, 8×A100).
Reward: final environment-state verifiers on real agent tasks (tool correctness + DB checks), with penalties for degeneration/truncation.
Training-set diversity: ~2.3k agent environments spanning 63% of industries and 76% of tools in the source corpus.

Benchmark results (improvement over the base model)

Only benchmarks where AgentMercury improves over the Qwen3.5-4B base are listed, with the absolute gain (Δ) over base. Evaluated with an OpenAI-compatible endpoint (sglang, 32k context), N repeats per cell.

Agentic / tool-use

Benchmark	Base	AgentMercury	Δ
BFCL	30.35	31.93	+1.58
τ³-bench	0.706	0.747	+0.041
τ²-bench	0.448	0.457	+0.009

Math & reasoning

Benchmark	Base	AgentMercury	Δ
AIME 2026	0.459	0.553	+0.094
HMMT 2026-02	0.285	0.356	+0.071
GPQA-Diamond	0.765	0.770	+0.005
Finance-Reasoning	0.563	0.571	+0.008
AA-Omniscience	−52.17	−51.67	+0.50

Code

Benchmark	Base	AgentMercury	Δ
LiveCodeBench (v5+v6)	0.366	0.435	+0.069
SciCode	0.226	0.260	+0.034

Writing

Benchmark	Base	AgentMercury	Δ
WritingBench	6.232	6.307	+0.075

Metrics use each benchmark's native scale (fractions 0–1, or the benchmark's own points). The largest, most consistent gains are on agentic tool-use (BFCL, τ-bench) and competition math / code (AIME, HMMT, LiveCodeBench).

Usage

Serving (sglang, recommended — matches evaluation)

python3 -m sglang.launch_server \
  --model-path Minbyul/AgentMercury-Qwen3.5-4B \
  --served-model-name agentmercury-qwen3.5-4b \
  --host 0.0.0.0 --port 30000 --tp 1 \
  --context-length 32768 \
  --reasoning-parser qwen3 --tool-call-parser qwen3_coder \
  --trust-remote-code

Then call the OpenAI-compatible endpoint at http://localhost:30000/v1 (supports tool calls).

transformers

from transformers import AutoModelForCausalLM, AutoProcessor
model = AutoModelForCausalLM.from_pretrained(
    "Minbyul/AgentMercury-Qwen3.5-4B",
    torch_dtype="bfloat16", device_map="auto", trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "Minbyul/AgentMercury-Qwen3.5-4B", trust_remote_code=True,
)

Training notes

The reward peaks around this checkpoint while the model stays clean (no repetition collapse, no context truncation). Continuing RL past this point raised response length and reintroduced degeneration/truncation without adding capability — so this clean-minimum checkpoint is released as the recommended weights.

License

Released under the Apache-2.0 license (see LICENSE).

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Safetensors

Model size

5B params

Tensor type

BF16

Collection including Minbyul/AgentMercury-Qwen3.5-4B

AgentMercury

Collection

AGENTMERCURY: YOUR AGENT CAN SYNTHESIZE VERIFIABLE ENVIRONMENTS FOR BUSINESS SCENARIOS AT SCALE • 1 item • Updated about 20 hours ago • 1