Instructions to use dolev31/ProactiveInquirer-Qwen3-8B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dolev31/ProactiveInquirer-Qwen3-8B-Merged with Transformers:

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

pipe = pipeline("text-generation", model="dolev31/ProactiveInquirer-Qwen3-8B-Merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dolev31/ProactiveInquirer-Qwen3-8B-Merged")
model = AutoModelForCausalLM.from_pretrained("dolev31/ProactiveInquirer-Qwen3-8B-Merged", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dolev31/ProactiveInquirer-Qwen3-8B-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dolev31/ProactiveInquirer-Qwen3-8B-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dolev31/ProactiveInquirer-Qwen3-8B-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dolev31/ProactiveInquirer-Qwen3-8B-Merged

SGLang

How to use dolev31/ProactiveInquirer-Qwen3-8B-Merged 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 "dolev31/ProactiveInquirer-Qwen3-8B-Merged" \
    --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": "dolev31/ProactiveInquirer-Qwen3-8B-Merged",
		"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 "dolev31/ProactiveInquirer-Qwen3-8B-Merged" \
        --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": "dolev31/ProactiveInquirer-Qwen3-8B-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dolev31/ProactiveInquirer-Qwen3-8B-Merged with Docker Model Runner:
```
docker model run hf.co/dolev31/ProactiveInquirer-Qwen3-8B-Merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ProactiveInquirer-Qwen3-8B-Merged

Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents

Ido Levy^1,2 · Asaf Yehudai¹ · Segev Shlomov¹ · Asaf Adi¹ · Leshem Choshen^1,2
¹IBM ²Weizmann Institute of Science

_{▶ The paper's example, step by step (22 seconds): the questioner trained with Q&D finds the account, the order with the boots and the size-8 boots, and the task is completed.}

The trained questioner from Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents, with its LoRA adapter merged into Qwen3-8B. It is a standard full-weight model: it loads without PEFT and serves with vLLM, SGLang or TGI like any Qwen3-8B.

The adapter, with the results, the training details, the limitations and a complete two-turn example: dolev31/ProactiveInquirer-Qwen3-8B.
Quantized for llama.cpp, Ollama and LM Studio: dolev31/ProactiveInquirer-Qwen3-8B-GGUF.

This is training seed 1, the adapter at the root of the adapter repository. The merge ran in float32 and the weights are stored in bfloat16. On the adapter card's two-turn example, greedy decoding with this model returns the adapter's output character for character.

Results

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.

How to use it

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.

import re

import torch
from huggingface_hub import hf_hub_download
from 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:

vllm serve dolev31/ProactiveInquirer-Qwen3-8B-Merged
# request body: {"messages": [{"role": "user", "content": "<the filled template>"}],
#                "chat_template_kwargs": {"enable_thinking": false}, "temperature": 0}

Limitations

The questioner's own limitations, from the paper: it has learned what to ask more readily than when to stop, the extra evidence it finds does not yet translate into better final answers, and its user-facing results come from a simulated customer, not from real people.
It is a component inside an agent, meant to be called with its prompt template. It is not a chat assistant, and it was trained and evaluated in English.
This is one training seed (seed 1), merged in float32 and stored in bfloat16. Its equality with the adapter was checked on the card's example, not on a benchmark.

Citation

@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}
}