Text Generation
PEFT
Safetensors
English
proactive-agents
information-seeking
question-asking
agents
retrieval-augmented-generation
multi-hop-qa
tau2-bench
dpo
lora
qwen3
Instructions to use dolev31/ProactiveInquirer-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dolev31/ProactiveInquirer-Qwen3-8B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "dolev31/ProactiveInquirer-Qwen3-8B") - Notebooks
- Google Colab
- Kaggle
Download example.py from dolev31/ProactiveInquirer-Qwen3-8B: direct link, hf CLI and curl.
- Browser
- Download file 2.34 kB
-
https://huggingface.co/dolev31/ProactiveInquirer-Qwen3-8B/resolve/main/example.py
- Command line
-
hf download hf://dolev31/ProactiveInquirer-Qwen3-8B/example.py
-
curl -L -o example.py https://huggingface.co/dolev31/ProactiveInquirer-Qwen3-8B/resolve/main/example.py
2.34 kB
| """The model card's example: two steps of the questioner on a two-hop question.""" | |
| import re | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| REPO = "dolev31/ProactiveInquirer-Qwen3-8B" | |
| tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B") | |
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", dtype=torch.bfloat16, device_map="auto") | |
| model = PeftModel.from_pretrained(model, REPO) # seed 1; add subfolder="seed2" for the second seed | |
| 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) | |
| 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." | |
| ) | |
| print(next_action( | |
| question=question, instructions=instructions, evidence="(nothing retrieved yet)", | |
| draft="(no draft yet)", history="(nothing asked yet)", | |
| )) | |
| evidence = ( | |
| "[3f2a9c1b7d4e] The Great Flamarion\n" | |
| "The Great Flamarion is a 1945 American film noir directed by Anthony Mann and starring " | |
| "Erich von Stroheim, Mary Beth Hughes and Dan Duryea." | |
| ) | |
| print(next_action( | |
| question=question, instructions=instructions, evidence=evidence, | |
| draft="The film was directed by Anthony Mann; his spouse is not yet known.", | |
| history="Q1: Who directed the film The Great Flamarion?\n" | |
| "A1: The Great Flamarion (1945) was directed by Anthony Mann.", | |
| )) | |