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Yangtze-ailab
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Model Details
Model Provider
Yangtze-ailab
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
An LDM runs a recurrent generate → select → evaluate → update loop in which an LLM
proposes candidates, a probabilistic surrogate turns observations into a posterior mean and
uncertainty, and an acquisition function selects the next experiment. This model is the
proposer, trained by full-parameter supervised fine-tuning on trajectories collected
from that loop across three scientific-discovery domains, so that the acquisition-guided
search policy is distilled into its weights. It emits a chain-of-thought trace followed by
a structured action (the proposed candidates).
Qwen/Qwen3.5-9Bqwen3_5 (chain-of-thought / thinking enabled)Deployment as the candidate proposer within the LDM acquisition loop, where the surrogate and acquisition function remain external. The model reads the evaluated history and task constraints and returns reasoning plus the next candidate(s).
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "Yangtze-ailab/LDM-SFT-Qwen3.5-9B-MixedScience"tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True)messages = [{"role": "system", "content": SYSTEM_PROMPT}, # proposer role + output contract{"role": "user", "content": SEARCH_STATE}, # evaluated history + constraints]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=2048, temperature=0.7)print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
The output is a <think> … </think> reasoning block followed by a JSON action describing
the proposed candidate(s).
Fine-tuned on the LDM mixed-science SFT corpus — proposal decisions collected from high-budget LDM test-time search across the three domains and rendered in Alpaca format. The corresponding public datasets are:
Released under the MIT license. The base model Qwen/Qwen3.5-9B remains subject to its own
license terms.