This model was fine-tuned using LoRA adapters on the ReAct subset of Agent-FLAN with the goal of making GPT-OSS more reliable at multi-step reasoning, tool selection, action-observation workflows, and structured agent behavior.

Overview

Large language models are often strong conversationalists but can struggle with:

Multi-step planning
Tool selection and invocation
ReAct-style reasoning workflows
Structured action generation
Separating reasoning from final responses

GPT-OSS AgentBoi adapts GPT-OSS-20B toward these agent-oriented tasks while remaining trainable on consumer hardware through parameter-efficient fine-tuning.

Model Details

Item	Value
Model Name	GPT-OSS AgentBoi
Author	shiv207
Base Model	unsloth/gpt-oss-20b-unsloth-bnb-4bit
Training Method	LoRA
Framework	Unsloth
Dataset	Agent-FLAN (ReAct subset)
Primary Task	Agentic Tool Use
Language	English
License	Apache 2.0

Training Data

The model was fine-tuned using examples from the Agent-FLAN dataset, specifically the ReAct-style instruction trajectories.

These examples teach the model to:

Break complex tasks into intermediate steps
Decide when tool usage is appropriate
Generate structured actions
Follow action-observation loops
Produce concise final responses

Training Setup

Training was performed using:

GPT-OSS-20B
Unsloth
TRL
LoRA adapters
Google Colab Tesla T4 GPU

The objective was to improve agentic behavior while keeping training accessible on limited hardware.

Intended Use

This model is intended for:

AI agents
Tool-calling systems
Research assistants
Retrieval-augmented generation workflows
Multi-step planning tasks
Agentic reasoning experiments

Potential applications include:

Search agents
Knowledge retrieval systems
Function-calling assistants
Research copilots
Workflow automation agents

Example

User

Search for the latest SpaceX launch and summarize it.

Expected Agent Behavior

Analyze the request.
Determine that external information is required.
Generate a structured search action.
Process retrieved information.
Produce a concise final answer.

The fine-tuning objective is to increase consistency in these workflows compared to the base model.

Loading the Model

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "shiv207/gpt_oss_AGENTBOI"
)

Limitations

Evaluated primarily through qualitative testing.
No formal benchmark suite was used.
Training utilized only a subset of Agent-FLAN.
Performance may vary on unseen tool schemas.
Not optimized for general-purpose instruction tuning beyond agent-oriented tasks.

Acknowledgments

This project builds upon the work of:

OpenAI for GPT-OSS and the Harmony conversation format.
Unsloth for efficient GPT-OSS fine-tuning support.
InternLM for the Agent-FLAN dataset.

Repository

Source code and training notebook:

GitHub: https://github.com/shiv207

Author

shiv207

If you find this project useful, feel free to open issues, share feedback, or build on top of it.