Instructions to use jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best with Transformers:

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

pipe = pipeline("text-generation", model="jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best")
model = AutoModelForCausalLM.from_pretrained("jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best

SGLang

How to use jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best 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 "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best" \
    --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": "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best",
		"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 "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best" \
        --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": "jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best with Docker Model Runner:
```
docker model run hf.co/jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best
```

Intern-S1-mini assay-transfer model: Carcinogens degree 24

Validation-selected checkpoint for the Carcinogens V27 degree-24 assay-transfer task. The checkpoint was selected at training step 500 using the joint ID/OOD level-macro MAE@3.

Provenance

Base model: jiosephlee/Intern-S1-mini-lm (revision fcb667c380ae01f57693a45b4b5c2d331052a107)
Training data: jiosephlee/assay-transfer-record-level-v27-carcinogens-general-intern
Dataset revision: 086291e05ee17c87fb3933075d1d57680acb2c3c
Training setup: 10 scheduled epochs, device batch size 4, gradient accumulation 4, 8 GPUs, FlashAttention-2, padding-free BFD packing, soft-target loss
Selection metric: Carcinogens/degree24/overall/knn_regression_id_ood_level_macro_mae_at_3
Best validation metric: 0.8675038371

The run was stopped after the best checkpoint had been preserved; this uploaded directory is that best checkpoint.

Held-out test comparison

Metrics are on the joint ID/OOD test panel. Lower MAE is better; higher Spearman and Top-1@3 are better.

Method	Macro MAE@3	Macro MAE@5	Spearman	Spearman@3	Top-1@3
This model	0.5587	0.5541	0.4197	-0.0286	0.1438
Morgan fingerprint (vanilla)	0.8142	0.8046	-0.0211	0.0506	0.0771
Morgan fingerprint (weighted)	0.7630	0.7327	-0.0177	0.0095	0.0685

The test evaluation is logged in Weights & Biases.

Usage

Load with the standard Transformers APIs and use trust_remote_code=True for the bundled tokenizer implementation.

Downloads last month: 227

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best

Base model

jiosephlee/Intern-S1-mini-lm

Finetuned

(30)

this model

jiosephlee
/

intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best

Intern-S1-mini assay-transfer model: Carcinogens degree 24

Provenance

Held-out test comparison

Usage

Model tree for jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best

Dataset used to train jiosephlee/intern-s1-mini-assay-transfer-record-level-v27-carcinogens-general-best