Instructions to use valiere/ISETrace-SFT-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use valiere/ISETrace-SFT-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="valiere/ISETrace-SFT-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("valiere/ISETrace-SFT-8B")
model = AutoModelForMultimodalLM.from_pretrained("valiere/ISETrace-SFT-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use valiere/ISETrace-SFT-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "valiere/ISETrace-SFT-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valiere/ISETrace-SFT-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/valiere/ISETrace-SFT-8B

SGLang

How to use valiere/ISETrace-SFT-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "valiere/ISETrace-SFT-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valiere/ISETrace-SFT-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "valiere/ISETrace-SFT-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valiere/ISETrace-SFT-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use valiere/ISETrace-SFT-8B with Docker Model Runner:
```
docker model run hf.co/valiere/ISETrace-SFT-8B
```

ISETrace-SFT-8B

English · 简体中文

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

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.

@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}
}