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

Libraries

How to use Willlzh/COFOS with Transformers:

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

pipe = pipeline("text-generation", model="Willlzh/COFOS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Willlzh/COFOS")
model = AutoModelForCausalLM.from_pretrained("Willlzh/COFOS")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Willlzh/COFOS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Willlzh/COFOS

SGLang

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

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

COFOS

COFOS is a domain-adapted language model for question answering about covalent organic frameworks (COFs), reactive oxygen species (ROS), oxygen-derived products, and photocatalytic or redox reaction mechanisms.

The model is intended to answer chemistry questions in a natural QA style while preserving important distinctions such as:

dominant ROS or oxygen-derived products versus secondary ROS or intermediates
H2O2 as an oxygen-derived product rather than a radical ROS
condition-dependent behavior under light, oxygen, water, sacrificial reagents, PMS, or related reaction environments
uncertainty when the available information is insufficient

Model Details

Model name: COFOS
Repository: Willlzh/COFOS
Model type: causal language model
Architecture family: Qwen-style decoder-only language model
Training method: QLoRA SFT followed by adapter merge
Training data: Willlzh/COFOS_data
Primary language: English
License: MIT

The uploaded checkpoint is a merged Transformers model directory. It can be loaded directly with AutoModelForCausalLM.from_pretrained() without separately loading a LoRA adapter.

Intended Use

COFOS is designed for research-oriented QA and drafting assistance around:

COF photocatalysis
ROS generation and assignment
oxygen reduction and H2O2 photoproduction
PMS-assisted oxidation mechanisms
evidence-aware explanations of dominant versus secondary species
chemistry and redox QA in an educational or literature-review setting

It is not a substitute for experimental validation, safety review, or expert chemical judgment.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Willlzh/COFOS"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

question = "What ROS does TT-T-COF generate under visible-light photocatalysis?"
messages = [
    {
        "role": "system",
        "content": (
            "You are COFOS, a natural QA assistant for covalent organic "
            "frameworks and reactive oxygen species. Answer directly and do "
            "not call H2O2 a radical ROS."
        ),
    },
    {"role": "user", "content": question},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Example Questions

What ROS does TT-T-COF generate under visible-light photocatalysis?

In a Fe3O4@TpMa/PMS photocatalytic system, which ROS are dominant for phenol degradation?

Why should H2O2 not be described as a radical ROS?

What should be considered before assigning O2·- or ·OH as the dominant ROS?

Training Data

The model was trained with a mixture of COFOS-specific and chemistry-oriented SFT data:

cofos_teacher_distill.jsonl: teacher-distilled COF/ROS QA data
cofos_rag_sft.jsonl: RAG-style samples with question, KG facts, retrieved evidence, and answer
chem_redox_sft.jsonl: English chemistry/redox QA samples
style_correction_sft.jsonl: answer-style correction samples focused on natural QA behavior and avoiding awkward evidence boilerplate

The dataset is available at Willlzh/COFOS_data.

Limitations

The model may still hallucinate details for materials or conditions that are not represented in its training data.
The model is not guaranteed to cite sources correctly unless used with an external retrieval system.
Mechanistic explanations should be treated as research assistance rather than final experimental conclusions.
The model should not be used for high-stakes chemical safety, medical, legal, or regulatory decisions.

Recommended Deployment Pattern

For the best COFOS experience, use the model as part of a local persistent assistant or a retrieval-augmented workflow:

user question
-> optional KG/BM25 retrieval
-> COFOS model
-> natural QA answer

The merged model can also be used directly for lightweight QA without retrieval, but RAG is recommended for record-specific material questions.

Citation

If you use COFOS in a project, please cite or link this model repository and the associated dataset:

Model: Willlzh/COFOS
Dataset: Willlzh/COFOS_data

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Safetensors

Model size

9B params

Tensor type

BF16

Willlzh
/

COFOS