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Model: ShogoMu/qwen25_7b_lora_agentbench_v6_e4
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+- u-10bei/sft_alfworld_trajectory_dataset_v5
+- ShogoMu/dbbench_u-10bei_sft_dataset_modified_v2
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- agent
+- tool-use
+- alfworld
+- dbbench
+---
+
+# qwen25_7b_lora_agentbench_v6_e4
+
+This repository provides a **merged model** fine-tuned from
+**Qwen/Qwen2.5-7B-Instruct**. The fine-tuning was performed using **LoRA + Unsloth** and the resulting adapter has been merged back into the base model weights.
+
+This repository contains **full model weights**, making it ready for inference
+without the need to load a separate adapter.
+
+## Training Objective
+
+This model is optimized for **multi-turn agent tasks**, specifically for
+ALFWorld (household navigation/interaction) and DBBench (database operations).
+
+The training process applied loss to **all assistant turns** in the multi-turn
+trajectories, allowing the model to learn not just final answers, but also
+intermediate reasoning (Thought), environment observation processing,
+action selection, and error recovery.
+
+## Training Configuration
+
+- **Base model:** Qwen/Qwen2.5-7B-Instruct
+- **Method:** LoRA (merged post-training)
+- **Max sequence length:** 2048
+- **Epochs:** 4
+- **Learning rate:** 2e-06
+- **LoRA Parameters:** r=64, alpha=128
+
+## Usage
+
+This model can be loaded using the standard `transformers` library or
+deployed with `vLLM` (recommended for evaluation).
+
+### Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "your_hf_id/your_repo_name"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)