初始化项目，由ModelHub XC社区提供模型

Model: simplex-ai-inc/LiteResearcher-4B-SFT
Source: Original Platform

README.md

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+---
+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.