jiosephlee
/

intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul

Instructions to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul with Transformers:

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

pipe = pipeline("text-generation", model="jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul")
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("jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul")
model = AutoModelForCausalLM.from_pretrained("jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul", 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 jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul 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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul"
# 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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul

SGLang

How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul 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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul" \
    --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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul",
		"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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul" \
        --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-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul with Docker Model Runner:
```
docker model run hf.co/jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul
```

Intern-S1-mini — Direct TDC Carcinogens

This repository contains the full-weight best_assay_ranking checkpoint from the Carcinogens fine-tuning run.

Provenance

Base model: jiosephlee/Intern-S1-mini-lm at fcb667c380ae01f57693a45b4b5c2d331052a107
Training dataset: jiosephlee/context-conditioned-molecule-transfer-v10.3-tdc-carcinogens-mixed-canonical-intern at revision c21748b1dcb036082014ac51f3bbcbd20872e5c2
Checkpoint-selection metric: ranking_validation/overall/knn_binary_macro_f1_at_5 = 0.7545
W&B run: view run (2xcwcpul)
Local run name: intern-s1-mini_context-conditioned-molecule-transfer-v10.3-tdc-pinned-carcinogens-mixed-canonical-soft-f1at5-b8x4-ga4-lr2e-5-packed-10ep

The repository includes the model shards, model configuration, tokenizer files, and the checkpoint's metric.json.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul

Base model

jiosephlee/Intern-S1-mini-lm

Finetuned

(30)

this model

Intern-S1-mini — Direct TDC Carcinogens

Provenance

Model tree for jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul

Dataset used to train jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-pinned-carcinogens-2xcwcpul