jiosephlee
/

intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST

Instructions to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST 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-v10-3-0-2-bioavailability-ma-BEST 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-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST")
model = AutoModelForCausalLM.from_pretrained("jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST",
		"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-v10-3-0-2-bioavailability-ma-BEST

SGLang

How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-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-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST with Docker Model Runner:
```
docker model run hf.co/jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST
```

BEST — Intern-S1-mini context-conditioned molecule transfer V10.3.0.2: Oral bioavailability

Validation-selected best checkpoint from the V10.3.0.2 mixed-continuous Oral bioavailability context-conditioned molecule-transfer run.

Provenance

Base model: jiosephlee/Intern-S1-mini-lm
Base model revision: fcb667c380ae01f57693a45b4b5c2d331052a107
Training data: V10.3.0.2 Bioavailability Ma mixed-continuous artifact
Intended dataset ID: jiosephlee/context-conditioned-molecule-transfer-v10.3.0.2-bioavailability-ma-mixed-continuous-intern (local artifact; not published as a Hub dataset)
Training: 10 epochs, seed 42, soft-target loss
Selection metric: validation knn_binary_macro_f1_at_5
Selected optimizer step: 160

Metrics

Split	Macro-F1@5	NDCG@5	Spearman
Validation	0.6401	0.7342	0.3561
Test	0.7066	0.7566	0.4096

The repository contains the full Transformers checkpoint and tokenizer files. metric.json is the complete validation-selection record stored with the checkpoint.

Downloads last month: 381

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-0-2-bioavailability-ma-BEST

Base model

jiosephlee/Intern-S1-mini-lm

Finetuned

(30)

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