Instructions to use hkqiu/PolyOpus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hkqiu/PolyOpus with Transformers:

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

pipe = pipeline("text-generation", model="hkqiu/PolyOpus")
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("hkqiu/PolyOpus")
model = AutoModelForCausalLM.from_pretrained("hkqiu/PolyOpus", 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 hkqiu/PolyOpus with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hkqiu/PolyOpus

SGLang

How to use hkqiu/PolyOpus 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 "hkqiu/PolyOpus" \
    --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": "hkqiu/PolyOpus",
		"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 "hkqiu/PolyOpus" \
        --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": "hkqiu/PolyOpus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hkqiu/PolyOpus with Docker Model Runner:
```
docker model run hf.co/hkqiu/PolyOpus
```

PolyOpus / README.md

hkqiu

Update README.md

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---
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).

## 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)

	---
	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).

	## 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)