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Model: CrystalReasoner/Qwen2.5-3B-CrysReas-NoEnergyTerm
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-3B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- crystal-generation
+- materials-science
+- qwen2.5
+- crystalreasoner
+---
+
+# CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
+
+CrystalReasoner (CrysReas) is an end-to-end LLM framework for generating crystal structures from natural language instructions. It uses supervised fine-tuning (SFT) to teach crystal-structure generation, thinking traces to introduce crystallographic and physical priors before coordinates, and reinforcement learning (RL) with verifiable rewards to improve validity, stability, and property conditioning.
+
+# Qwen2.5-3B-CrysReas-NoEnergyTerm
+
+## Quick Start
+
+You can use this model directly with the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
+import torch
+
+model_id = "CrystalReasoner/Qwen2.5-3B-CrysReas-NoEnergyTerm"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    config=config,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+
+messages = [
+    {"role": "user", "content": "Below is a description of a bulk material. The chemical formula is NaCl. The bulk_modulus is about 100 GPa. Generate a description of the lengths and angles of the lattice vectors and then the element type and coordinates for each atom within the lattice:"},
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+    use_cache=True,
+)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
+print(generated_text)
+```
+
+If you want the generated structure in pymatgen Structure format, please use this script after the previous generation:
+
+```python
+def get_structure(generated_text: str):
+    import re
+    from pymatgen.core import Lattice, Structure
+
+    cif_match = re.search(r'<CIF>(.*?)</CIF>', generated_text, re.DOTALL)
+    if cif_match:
+        generated_text = cif_match.group(1)
+
+    lines = [line.strip() for line in generated_text.strip().split('\n') if line.strip()]
+    if lines and not re.match(r'^[-+0-9.eE\s]+$', lines[0]):
+        lines = lines[1:]
+
+    lengths = list(map(float, lines[0].split()))
+    angles = list(map(float, lines[1].split()))
+    lattice = Lattice.from_parameters(*lengths, *angles)
+
+    species = []
+    coords = []
+    for line in lines[2:]:
+        parts = line.split()
+        species.append(parts[0])
+        coords.append([float(parts[2]), float(parts[3]), float(parts[4])])
+
+    structure = Structure(lattice, species, coords)
+    return structure
+
+structure = get_structure(generated_text)
+print(structure)
+```