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legoskier/qwen3-4b-agent-trajectory-lora_2
qwen3-4b-agent-trajectory-lora_2 is a text generation model from legoskier. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using LoRA + Unsloth.
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Updated Feb 18, 2026
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From the Hugging Face model README
This repository provides a LoRA adapter fine-tuned from
Qwen/Qwen3-4B-Instruct-2507 using LoRA + Unsloth.
This repository contains LoRA adapter weights only.
The base model must be loaded separately.
Trained to improve multi-turn agent task performance on
ALFWorld (household tasks) and DBBench (database operations).
Loss is applied to all assistant turns, enabling the model to learn
environment observation, action selection, tool use, and error recovery.
| Item | Value |
|---|---|
| Base model | Qwen/Qwen3-4B-Instruct-2507 |
| Method | LoRA (full precision base, bf16) |
| Datasets | ALFWorld v5 + DBBench v4 |
| Max sequence length | 8192 |
| Epochs | 2 |
| Learning rate | 5e-06 |
| LoRA r / alpha | 128 / 256 |
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "legoskier/qwen3-4b-agent-trajectory-lora_2"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
base, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter)
Training datasets:
u-10bei/sft_alfworld_trajectory_dataset_v5u-10bei/dbbench_sft_dataset_react_v4This repository does not redistribute the datasets.
Users must comply with the respective dataset licenses and the base model's terms of use.