Instructions to use simplex-ai-inc/LiteResearcher-4B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simplex-ai-inc/LiteResearcher-4B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="simplex-ai-inc/LiteResearcher-4B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simplex-ai-inc/LiteResearcher-4B-SFT")
model = AutoModelForCausalLM.from_pretrained("simplex-ai-inc/LiteResearcher-4B-SFT", 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 simplex-ai-inc/LiteResearcher-4B-SFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/simplex-ai-inc/LiteResearcher-4B-SFT

SGLang

How to use simplex-ai-inc/LiteResearcher-4B-SFT 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 "simplex-ai-inc/LiteResearcher-4B-SFT" \
    --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": "simplex-ai-inc/LiteResearcher-4B-SFT",
		"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 "simplex-ai-inc/LiteResearcher-4B-SFT" \
        --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": "simplex-ai-inc/LiteResearcher-4B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simplex-ai-inc/LiteResearcher-4B-SFT with Docker Model Runner:
```
docker model run hf.co/simplex-ai-inc/LiteResearcher-4B-SFT
```

LiteResearcher-4B-SFT / README.md

wanlilll

Fix base model: Qwen3-4B-Thinking-2507 (not Qwen3-4B); add LR/warmup/global-bs details

8294d1c verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

3.48 kB

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	- zh
	base_model:
	- Qwen/Qwen3-4B-Thinking-2507
	tags:
	- agent
	- deep-research
	- react
	- search
	- sft
	- cold-start
	- thinking
	- LiteResearcher
	datasets:
	- simplex-ai-inc/LiteResearcher-Data
	---

	# LiteResearcher-4B-SFT

	This is the SFT cold-start checkpoint for [LiteResearcher](https://github.com/simplex-ai-inc/LiteResearcher) —
	a scalable agentic RL training framework for deep-research agents.

	It is the initial policy used to launch the two-stage curriculum RL training that
	produces the final [`simplex-ai-inc/LiteResearcher-4B`](https://huggingface.co/simplex-ai-inc/LiteResearcher-4B) model.

	> If you are looking for the final RL model, please use
	> [`simplex-ai-inc/LiteResearcher-4B`](https://huggingface.co/simplex-ai-inc/LiteResearcher-4B).
	> If you want to reproduce the RL training from scratch, this is the checkpoint you need.

	## Model details

	- Base model: [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
	- Architecture: `Qwen3ForCausalLM` (36 layers, hidden 2560, 32 heads, GQA 8 KV heads)
	- Max position embeddings: 262,144 (RoPE θ = 5,000,000)
	- Precision: `bfloat16`
	- Total params: ~4B
	- Training framework: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)

	## Training recipe

	\| Item \| Value \|
	\|---\|---\|
	\| Stage \| SFT (cold-start before RL) \|
	\| Base model \| `Qwen/Qwen3-4B-Thinking-2507` \|
	\| Dataset \| [`simplex-ai-inc/LiteResearcher-Data`](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data) (~68.2k SFT trajectories) \|
	\| Max sequence length \| 64K (`cutoff_len=65536`) \|
	\| Global batch size \| 128 (per-device bs 2 × grad-accum 8 × 8 GPUs) \|
	\| Epochs \| 1 \|
	\| Optimizer steps \| 533 \|
	\| Learning rate \| 2.0e-5, cosine, 10% warmup \|
	\| Final train loss \| ≈ 0.447 (starting loss ≈ 1.19) \|

	The SFT trajectories teach the model the ReAct `think` → `search` → `visit` → `answer`
	loop and the strict `<answer>...</answer>` output contract used by the RL environment.
	Because the base is the Thinking-2507 variant, the model preserves long
	chain-of-thought behavior inside `<think>...</think>` blocks, which is what the
	downstream RL curriculum builds on.

	## How to use

	### As the initial policy for RL (recommended use)

	```bash
	# In the LiteResearcher training scripts (Training/ folder of the repo)
	export MODEL_PATH=$(hf download simplex-ai-inc/LiteResearcher-4B-SFT \
	--local-dir ./literesearcher_sft)
	```

	Then follow the Stage-1 / Stage-2 RL instructions in the
	[LiteResearcher repository](https://github.com/simplex-ai-inc/LiteResearcher).

	### Stand-alone inference

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "simplex-ai-inc/LiteResearcher-4B-SFT"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, torch_dtype=torch.bfloat16, device_map="auto"
	)
	```

	The model expects the same ReAct system prompt and tool schema used by
	LiteResearcher (see `Inference/` in the repo).

	## Citation

	If you use this checkpoint in academic work, please cite the LiteResearcher
	project — see the [GitHub README](https://github.com/simplex-ai-inc/LiteResearcher) for the BibTeX entry.

	## License

	Apache-2.0, inheriting from the [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) base model.