A Qwen3-8B agent supervised-fine-tuned on ISETrace — execution-grounded, multi-turn OS-agent trajectories synthesized by the ISE (Intent → Simulate → Execute) paradigm.

ISETrace-SFT-8B is a full-parameter SFT of Qwen3-8B on the ISETrace corpus: 23,132 multi-turn OS-agent trajectories in which every tool call was executed against a live, isolated operating-system workspace. The model is trained for long, coherent, tool-using task completion on macOS/Linux terminals.

📄 Paper: arXiv:2606.11520
🤗 Training data: https://huggingface.co/datasets/valiere/ISETrace
🛠️ Pipeline (umbrella): https://github.com/Valiere01/ISE-Trace
🧩 Stage 1 — intent construction: https://github.com/NairongZheng/intent_creator
⚙️ Stage 2+3 — simulation + execution: https://github.com/NairongZheng/openclaw_gen_data

Model details

Base model: Qwen/Qwen3-8B (8.2B params, 36 layers, GQA 32/8 heads, YaRN rope scaling)
Training: Full-parameter supervised fine-tuning on the ISETrace trajectory corpus
Context: up to 40,960 tokens (training max_length); base supports 131,072 with YaRN
Precision: bfloat16
Format: standard HuggingFace Qwen3ForCausalLM safetensors — loads directly with transformers

The model is trained for multi-turn OS/tool-use agent interaction: it emits <tool_call>...</tool_call> blocks, consumes <tool_response>...</tool_response>, and sustains long task-completion dialogues. It uses the Qwen3 chat template (shipped as chat_template.jinja).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "valiere/ISETrace-SFT-8B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [
    {"role": "user", "content": "List the largest 3 files under /var/log and tell me their sizes."},
]
inputs = tok.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.8)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

For tool-use, pass your tool schemas via tools= in apply_chat_template; the model produces OpenAI-style tool calls. Serve with vLLM / SGLang for production throughput.

Intended use & limitations

ISETrace-SFT-8B targets macOS/Linux OS-terminal agent tasks — shell execution, file operations, and multi-step tool-use under a user simulator. It does not cover Windows, GUI-based interaction, or browser automation. As a research checkpoint it inherits the biases and knowledge cutoff of Qwen3-8B and the distribution of the ISETrace corpus.

Tool calls executed by an agent built on this model run real commands; sandbox appropriately before granting filesystem or network access.

License & citation

This model is a derivative of Qwen3-8B and is released under the Apache 2.0 license, consistent with the base model. The ISETrace training data is released separately under CC BY 4.0.

bibtex

@misc{isetrace2026,
  title        = {From Intent to Trajectory: Execution-Grounded Multi-Turn Data Synthesis for OS Agents},
  author       = {Valiere01},
  year         = {2026},
  howpublished = {\url{https://github.com/Valiere01/ISE-Trace}},
  note         = {Paper link forthcoming}
}