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Model: plotMaker/qwen25-7b-sft-merged-v5v6-a50
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+  - u-10bei/sft_alfworld_trajectory_dataset_v2
+  - u-10bei/sft_alfworld_trajectory_dataset_v3
+  - u-10bei/sft_alfworld_trajectory_dataset_v4
+  - u-10bei/sft_alfworld_trajectory_dataset_v5
+  - u-10bei/dbbench_sft_dataset_react
+  - u-10bei/dbbench_sft_dataset_react_v2
+  - u-10bei/dbbench_sft_dataset_react_v3
+  - u-10bei/dbbench_sft_dataset_react_v4
+language:
+  - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - sft
+  - agent
+  - tool-use
+  - alfworld
+  - dbbench
+---
+
+# qwen25-7b-sft-merged-v5v6-a50
+
+This repository provides a **fully merged model** fine-tuned from
+**Qwen2.5-7B-Instruct** using **QLoRA + Unsloth**.
+
+Two SFT models (v5 and v6) were trained independently, then combined via
+weight interpolation (alpha=0.5). This is a **complete model** — no adapters
+or additional weights are needed.
+
+## 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/Qwen2.5-7B-Instruct
+- Method: QLoRA (4-bit) + Unsloth, merged into base model
+- Max sequence length: 2048
+- Epochs: 2
+- Learning rate: 5e-5
+- LoRA: r=32, alpha=64
+- Post-training: weight interpolation of v5 and v6 (alpha=0.5)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "plotMaker/qwen25-7b-sft-merged-v5v6-a50"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+```
+
+## References                                                                                                 
+                                                                                                                
+  - [Model Soups (Wortsman et al., 2022)](https://arxiv.org/abs/2203.05482) — Weight interpolation of fine-tuned
+   models                                                   
+  - [LoRA (Hu et al., 2021)](https://arxiv.org/abs/2106.09685) — Low-Rank Adaptation
+  - [NEFTune (Jain et al., 2024)](https://arxiv.org/abs/2310.05914) — Noisy embedding fine-tuning
+  - [rsLoRA (Kalajdzievski, 2023)](https://arxiv.org/abs/2312.03732) — Rank-stabilized LoRA scaling
+  - [ALFWorld (Shridhar et al., 2021)](https://arxiv.org/abs/2010.03768) — Interactive text-world environments
+  - [ReAct (Yao et al., 2023)](https://arxiv.org/abs/2210.03629) — Reasoning and acting in LLMs
+
+## Sources & Terms (IMPORTANT)
+
+Training data:
+- u-10bei/sft_alfworld_trajectory_dataset_v2 ~ v5
+- u-10bei/dbbench_sft_dataset_react ~ v4
+
+Base model: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+
+This repository does NOT redistribute the dataset.
+Users must comply with the dataset license and base model terms.