初始化项目，由ModelHub XC社区提供模型

Model: SELEE/qwen3-4b-agent-v3
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/sft_alfworld_trajectory_dataset_v5
+- u-10bei/sft_alfworld_trajectory_dataset_v4
+- u-10bei/dbbench_sft_dataset_react_v4
+- u-10bei/dbbench_sft_dataset_react_v3
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- agent
+- tool-use
+- alfworld
+- dbbench
+---
+
+# qwen3-4b-agent-full-v3
+
+This repository provides a **fully fine-tuned model** based on
+**Qwen/Qwen3-4B-Instruct-2507**.
+
+Because this model underwent full parameter fine-tuning, this repository contains the **full model weights**.
+You can load it directly without needing to merge it with the base model.
+
+## 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: full
+- Max sequence length: 4096
+- Epochs: 2
+- Learning rate: 2e-06
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+base = "Qwen/Qwen3-4B-Instruct-2507"
+
+tokenizer = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(
+    base,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+```
+
+## Sources & Terms (IMPORTANT)
+
+Training data: u-10bei/sft_alfworld_trajectory_dataset_v4, u-10bei/sft_alfworld_trajectory_dataset_v5, u-10bei/dbbench_sft_dataset_react_v4, u-10bei/dbbench_sft_dataset_react_v3
+
+Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
+Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.