54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
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---
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base_model: melon1891/agentbench-qwen3-4b-lr5e6-20260224v2
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datasets:
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- melon1891/reasoning-chain-distilled-317
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- agent
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- tool-use
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- alfworld
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- dbbench
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---
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# agentbench-qwen3-4b-2stage-reasoning-20260228
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A full model fine-tuned from **melon1891/agentbench-qwen3-4b-lr5e6-20260224v2** using LoRA + Unsloth, with the adapter merged into the base model.
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## Training Objective
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This model is trained to improve **multi-turn agent task performance**
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on ALFWorld (household tasks) and DBBench (database operations).
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Loss is applied to **all assistant turns** in the multi-turn trajectory,
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enabling the model to learn environment observation, action selection,
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tool use, and recovery from errors.
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## Training Configuration
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- Base model: melon1891/agentbench-qwen3-4b-lr5e6-20260224v2
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- Method: LoRA (merged into base)
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- Max sequence length: 8192
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- Epochs: 3
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- Learning rate: 1e-06
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- LoRA: r=16, alpha=32
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
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tokenizer = AutoTokenizer.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
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```
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## Sources & Terms (IMPORTANT)
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Training data: melon1891/reasoning-chain-distilled-317
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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