Instructions to use AdithyaSK/data-agent-4b-normal-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdithyaSK/data-agent-4b-normal-best with Transformers:

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

pipe = pipeline("text-generation", model="AdithyaSK/data-agent-4b-normal-best")
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("AdithyaSK/data-agent-4b-normal-best")
model = AutoModelForMultimodalLM.from_pretrained("AdithyaSK/data-agent-4b-normal-best", 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 AdithyaSK/data-agent-4b-normal-best with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdithyaSK/data-agent-4b-normal-best

SGLang

How to use AdithyaSK/data-agent-4b-normal-best 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 "AdithyaSK/data-agent-4b-normal-best" \
    --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": "AdithyaSK/data-agent-4b-normal-best",
		"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 "AdithyaSK/data-agent-4b-normal-best" \
        --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": "AdithyaSK/data-agent-4b-normal-best",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdithyaSK/data-agent-4b-normal-best with Docker Model Runner:
```
docker model run hf.co/AdithyaSK/data-agent-4b-normal-best
```

data-agent-4b-normal-best / README.md

AdithyaSK HF Staff

v0: model card

2f52af8 verified 6 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-4B
	tags:
	- reinforcement-learning
	- grpo
	- trl
	- harbor
	- data-agent
	- agentic
	pipeline_tag: text-generation
	library_name: transformers
	---

	# data-agent-4b-normal-best (v0)

	A 4B data-science agent finetuned from [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) with
	GRPO (online RL) to solve data-analysis tasks in a sandboxed bash environment. This repo holds
	the best-eval checkpoint (peak pass@4, step 200) of the `4b-normal` run.

	## Training
	- Method: GRPO (Group Relative Policy Optimization) via [TRL](https://github.com/huggingface/trl).
	- Environment: [Harbor](https://github.com/huggingface/trl) task spec + E2B cloud sandboxes; single `bash` tool, answer submitted to `/workdir/answer.txt`.
	- Dataset: [`AdithyaSK/data_agent_rl_environment_train`](https://huggingface.co/datasets/AdithyaSK/data_agent_rl_environment_train).
	- Schedule: 1 epoch (1119 steps), 8 generations/prompt, KL-anchored to the reference. Tasks were presented in random order (standard).
	- This checkpoint: step 200 (best-eval).

	## Evaluation
	Agentic pass@k on the held-out [`data_agent_rl_environment_eval`](https://huggingface.co/datasets/AdithyaSK/data_agent_rl_environment_eval) suite (366 tasks, 4 samples/task, unbiased estimator):

	\| metric \| base (Qwen3.5-4B) \| this model \| Δ \|
	\|---\|---\|---\|---\|
	\| pass@1 \| 0.600 \| 0.660 \| +0.060 \|
	\| pass@2 \| 0.677 \| 0.745 \| +0.068 \|
	\| pass@3 \| 0.715 \| 0.779 \| +0.064 \|
	\| pass@4 \| 0.740 \| 0.802 \| +0.062 \|

	## Usage
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	m = AutoModelForCausalLM.from_pretrained("AdithyaSK/data-agent-4b-normal-best", revision="v0", torch_dtype="bfloat16")
	tok = AutoTokenizer.from_pretrained("AdithyaSK/data-agent-4b-normal-best", revision="v0")
	```

	Part of the data-agent v0* release. Served non-thinking with a single `bash` tool (Qwen tool-calling).*