Add PolyOpus model card and usage example

README.md

@@ -1,3 +1,76 @@
 ---
-license: apache-2.0
+language:
+  - en
+  - zh
+library_name: transformers
+pipeline_tag: text-generation
+base_model: deepseek-ai/deepseek-llm-7b-chat
+tags:
+  - chemistry
+  - polymers
+  - materials-science
+  - text-generation
 ---
+
+# PolyOpus
+
+PolyOpus is a polymer-specialized language model built by supervised fine-tuning of [DeepSeek-LLM-7B-Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat). It serves as the specialist model in the [PolySea framework](https://github.com/hkqiu/PolySea).
+
+PolyOpus 是面向聚合物科学的专用语言模型，支持性质预测、指定带隙的结构生成和聚合物知识问答。
+
+## Supported tasks
+
+- Predict glass-transition temperature, crystalline band gap and atomization energy.
+- Classify polyimide heat resistance using Tg-derived categories.
+- Generate polymer structures for a specified band-gap target.
+- Answer polymer-science questions in English or Chinese.
+
+For property prediction, provide the requested property and a polymer repeat-unit SMILES. The `*` symbols indicate polymerization attachment points.
+
+## Model details
+
+- Approximately 7B parameters; `LlamaForCausalLM` architecture.
+- A complete merged FP16 checkpoint in three Safetensors shards. No separate LoRA adapter is required for inference.
+- A 4,096-token context window, with the tokenizer and chat template included.
+
+## Quick start
+
+Install `torch`, `transformers` and `accelerate`, then run:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "hkqiu/PolyOpus"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.float16, device_map="auto"
+)
+
+messages = [{
+    "role": "user",
+    "content": "Predict the Tg of the following SMILES: *CC*"
+}]
+prompt = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(
+    prompt, add_special_tokens=False, return_tensors="pt"
+).to(model.device)
+
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs, max_new_tokens=128, do_sample=True,
+        temperature=0.7, top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+answer = outputs[0, inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(answer, skip_special_tokens=True))
+```
+
+Predictions and generated structures require independent validation before use in experiments or materials selection.
+
+## Links
+
+- [PolySea source code](https://github.com/hkqiu/PolySea)
+- [Base model](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)