base_model, datasets, language, license, library_name, pipeline_tag, tags
| base_model | datasets | language | license | library_name | pipeline_tag | tags | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| melon1891/agentbench-qwen3-4b-lr5e6-20260224v2 |
|
|
apache-2.0 | transformers | text-generation |
|
agentbench-qwen3-4b-2stage-reasoning-20260228
A full model fine-tuned from melon1891/agentbench-qwen3-4b-lr5e6-20260224v2 using LoRA + Unsloth, with the adapter merged into 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: melon1891/agentbench-qwen3-4b-lr5e6-20260224v2
- Method: LoRA (merged into base)
- Max sequence length: 8192
- Epochs: 3
- Learning rate: 1e-06
- LoRA: r=16, alpha=32
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
tokenizer = AutoTokenizer.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
Sources & Terms (IMPORTANT)
Training data: melon1891/reasoning-chain-distilled-317
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.