GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
dolev31
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
dolev31
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
The results are the trained questioner's, as the paper reports them: see the adapter card's Results. This merged model reproduces the adapter's output on that card's example, as the paragraph above says.
The questioner reads the prompt template it was trained on, in prompts/, and replies
with one JSON action per step: {"action": "ask", "question": ...} or {"action": "stop", ...}.
Keep Qwen3's thinking off, as in training.
python
import re import torchfrom huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, AutoTokenizer REPO = "dolev31/ProactiveInquirer-Qwen3-8B-Merged"tok = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained(REPO, dtype=torch.bfloat16, device_map="auto")template = open(hf_hub_download(REPO, "prompts/inquirer_prompted.txt"), encoding="utf-8").read()placebo = open(hf_hub_download(REPO, "prompts/fragment_user_channel_placebo.txt"), encoding="utf-8").read() def next_action(**state): fields = dict(state, user_channel=placebo.strip()) prompt = re.sub(r"\{\{(\w+)\}\}", lambda m: str(fields[m.group(1)]), template) ids = tok.apply_chat_template( [{"role": "user", "content": prompt}], add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True, ).to(model.device) out = model.generate(**ids, max_new_tokens=200, do_sample=False) return tok.decode(out[0, ids["input_ids"].shape[1] :], skip_special_tokens=True) print(next_action( question="Who was the spouse of the director of the film The Great Flamarion?", instructions="Answer the question using a closed pool of 20 paragraphs. You may issue retrieval " "queries against that pool before answering; several paragraphs are distractors, and the answer " "usually requires composing facts from more than one of them.", evidence="(nothing retrieved yet)", draft="(no draft yet)", history="(nothing asked yet)",))# {"action": "ASK", "question": "Who directed the film The Great Flamarion?", "rationale": "Identify the director to later find their spouse"}With vLLM, serve it and send the filled template as the user message, with thinking off:
bash
vllm serve dolev31/ProactiveInquirer-Qwen3-8B-Merged# request body: {"messages": [{"role": "user", "content": "<the filled template>"}],# "chat_template_kwargs": {"enable_thinking": false}, "temperature": 0}bibtex
@article{levy2026asking, title = {Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents}, author = {Levy, Ido and Yehudai, Asaf and Shlomov, Segev and Adi, Asaf and Choshen, Leshem}, journal = {arXiv preprint arXiv:2609.37236}, url = {https://arxiv.org/abs/2609.37236}, year = {2026}}Apache-2.0, like the base model Qwen3-8B.
import re import torchfrom huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, AutoTokenizer REPO = "dolev31/ProactiveInquirer-Qwen3-8B-Merged"tok = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained(REPO, dtype=torch.bfloat16, device_map="auto")template = open(hf_hub_download(REPO, "prompts/inquirer_prompted.txt"), encoding="utf-8").read()placebo = open(hf_hub_download(REPO, "prompts/fragment_user_channel_placebo.txt"), encoding="utf-8").read() def next_action(**state): fields = dict(state, user_channel=placebo.strip()) prompt = re.sub(r"\{\{(\w+)\}\}", lambda m: str(fields[m.group(1)]), template) ids = tok.apply_chat_template( [{"role": "user", "content": prompt}], add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True, ).to(model.device) out = model.generate(**ids, max_new_tokens=200, do_sample=False) return tok.decode(out[0, ids["input_ids"].shape[1] :], skip_special_tokens=True) print(next_action( question="Who was the spouse of the director of the film The Great Flamarion?", instructions="Answer the question using a closed pool of 20 paragraphs. You may issue retrieval " "queries against that pool before answering; several paragraphs are distractors, and the answer " "usually requires composing facts from more than one of them.", evidence="(nothing retrieved yet)", draft="(no draft yet)", history="(nothing asked yet)",))# {"action": "ASK", "question": "Who directed the film The Great Flamarion?", "rationale": "Identify the director to later find their spouse"}vllm serve dolev31/ProactiveInquirer-Qwen3-8B-Merged# request body: {"messages": [{"role": "user", "content": "<the filled template>"}],# "chat_template_kwargs": {"enable_thinking": false}, "temperature": 0}@article{levy2026asking, title = {Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents}, author = {Levy, Ido and Yehudai, Asaf and Shlomov, Segev and Adi, Asaf and Choshen, Leshem}, journal = {arXiv preprint arXiv:2609.37236}, url = {https://arxiv.org/abs/2609.37236}, year = {2026}}