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valiere
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Model Details
Model Provider
valiere
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
max_length); base supports 131,072 with YaRNQwen3ForCausalLM safetensors — loads directly with transformersThe 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).
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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.
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.
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}}from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))@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}}