Instructions to use StarLiu714/GPT-oss-MOFinder-Yearwise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StarLiu714/GPT-oss-MOFinder-Yearwise with Transformers:

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

pipe = pipeline("text-generation", model="StarLiu714/GPT-oss-MOFinder-Yearwise")
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("StarLiu714/GPT-oss-MOFinder-Yearwise")
model = AutoModelForCausalLM.from_pretrained("StarLiu714/GPT-oss-MOFinder-Yearwise", 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 StarLiu714/GPT-oss-MOFinder-Yearwise with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StarLiu714/GPT-oss-MOFinder-Yearwise"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StarLiu714/GPT-oss-MOFinder-Yearwise",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StarLiu714/GPT-oss-MOFinder-Yearwise

SGLang

How to use StarLiu714/GPT-oss-MOFinder-Yearwise 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 "StarLiu714/GPT-oss-MOFinder-Yearwise" \
    --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": "StarLiu714/GPT-oss-MOFinder-Yearwise",
		"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 "StarLiu714/GPT-oss-MOFinder-Yearwise" \
        --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": "StarLiu714/GPT-oss-MOFinder-Yearwise",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use StarLiu714/GPT-oss-MOFinder-Yearwise with Docker Model Runner:
```
docker model run hf.co/StarLiu714/GPT-oss-MOFinder-Yearwise
```

GPT-oss-MOF-Yearwise: pre 2017 training

This repository shows a series of AutoModelForCausalLM which are respectively trained on literature data with different publication year range. We reports their forecasting performance on next-period published experimental data.

This 1999-2017 branch specifically trained GPT on MOF literatures prior than 2017.

Result

Train set spans	Inference	Test size	Accuracy
~2017	Holdout	1320P+ 1275N	0.73295
~2017	22 questions	11P + 11N	17/22
~2017	2018-2020	2232P + 1922N	0.66635

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "GPT-oss-MOF-Yearwise"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

To go throught the quick start details for reprduction, see the official released MOFinder main checkpoint which is well-trained on full PN data.

Citation and license

When using these weights, cite MOFinder, identify StarLiu714/GPT-oss-MOF and the Hub revision used, and reference the GPT-oss-20B base model. This release is licensed under the GNU General Public License v3.0; see LICENSE. The underlying GPT-oss-20B model retains its upstream Apache-2.0 license, reproduced in LICENSE.base-model. This release includes MOF fine-tuning changes merged into the base model and a BF16 weight export. MOFinder codebase is separately licensed under MIT.

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Model size

21B params

Tensor type

BF16

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MOF synthesis specific finetuned GPT-oss-20B model, with its ablations and time-series experiment assets. • 5 items • Updated 12 days ago