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suhas9545/
Qwen2.5-3B-SWE-Agent-QLoRA

Name: Qwen2.5-3B-SWE-Agent-QLoRA
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Qwen2.5-3B-SWE-Agent-QLoRA

A QLoRA adapter trained on top of Qwen2.5-Coder-3B-Instruct for software engineering agent workflows, repository reasoning, and structured tool-based coding tasks.

This adapter is optimized for:

multi-step repository reasoning
debugging workflows
codebase navigation
structured tool generation
autonomous coding agents
SWE-agent style trajectories
JSON-based tool planning

Base Model

Qwen/Qwen2.5-Coder-3B-Instruct

Base model link:

https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct

Training Dataset

Trained on:

suhas9545/Multi_Turn_SWE_dataset

Dataset link:

https://huggingface.co/datasets/suhas9545/Multi_Turn_SWE_dataset

The dataset contains structured multi-turn software engineering trajectories derived from SWE-agent style repository interactions and tool-use workflows.

Quantization & Training

This adapter was trained using QLoRA with:

4-bit NF4 quantization
PEFT LoRA adapters
bitsandbytes
Transformers

Recommended inference dtype:

float16
bfloat16

Intended Use

Recommended for:

coding assistants
SWE-agents
autonomous debugging systems
repository interaction agents
tool-calling agents
structured JSON generation
software engineering research

Example Usage

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

BASE_MODEL = "Qwen/Qwen2.5-Coder-3B-Instruct"
ADAPTER = "YOUR_USERNAME/Qwen2.5-3B-SWE-Agent-QLoRA"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto",
    quantization_config=bnb_config,
)

model = PeftModel.from_pretrained(model, ADAPTER)

prompt = "Fix failing tests in a Python repository."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Prompting Style

The model performs best with concise task-oriented prompts.

Examples:

Fix failing tests in the repository.

Create a JSON tool plan to debug the issue.

Analyze the codebase and modify the failing function.

Limitations

Generated commands and patches should be reviewed before execution.
The model may hallucinate repository structure or tool outputs.
Performance depends heavily on prompt quality and inference settings.
Optimized primarily for coding and SWE-agent style tasks rather than general conversation.

Citation

text

@article{baumann2026swechat,
  title={SWE-chat: Coding Agent Interactions From Real Users in the Wild},
  author={Baumann, Joachim and Padmakumar, Vishakh and Li, Xiang and Yang, John and Yang, Diyi and Koyejo, Sanmi},
  year={2026},
  journal={arXiv preprint arXiv:2604.20779},
  url={https://arxiv.org/abs/2604.20779}
}

suhas9545/Qwen2.5-3B-SWE-Agent-QLoRA