qwen3-4b-agent-sft-true/README.md

base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
Qwen/Qwen3-4B-Instruct-2507	Gen-Verse/Open-AgentRL-SFT-3K	en	apache-2.0	transformers	text-generation	agent tool-use sft multi-turn code-interpreter open-agentrl

Qwen3-4B-Agent-SFT-True

This repository contains a full fine-tuned model (not LoRA adapter) based on Qwen3-4B-Instruct-2507, trained with multi-turn agentic SFT using the Open-AgentRL framework (verl FSDP SFT Trainer).

Training Configuration

Parameter	Value
Base model	Qwen/Qwen3-4B-Instruct-2507
Method	Full fine-tuning (FSDP, bfloat16)
Max sequence length	32,768
Epochs	10
Train batch size	16
Micro batch size per GPU	1
Truncation	right
Trainer	verl.trainer.fsdp_sft_trainer

Dataset

• Name: Gen-Verse/Open-AgentRL-SFT-3K
• Samples: 3,000 multi-turn conversations
• Source: Original Open-AgentRL SFT dataset (real End-to-End agentic trajectories)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "y-ohtani/qwen3-4b-agent-sft-true"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Solve the equation x^2 - 5x + 6 = 0 step by step."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sources & Terms

Component	Source	License
Base model	Qwen/Qwen3-4B-Instruct-2507	Apache-2.0
SFT dataset	Gen-Verse/Open-AgentRL-SFT-3K	--
Training framework	Open-AgentRL (verl)	Apache-2.0

Users must comply with the base model license and dataset terms.