jiosephlee
/

intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-bioavailability-ma-best

Instructions to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-bioavailability-ma-best")
model = AutoModelForCausalLM.from_pretrained("jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-bioavailability-ma-best

SGLang

How to use jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-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-tdc-v2-hp-bioavailability-ma-best with Docker Model Runner:
```
docker model run hf.co/jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-bioavailability-ma-best
```

Intern-S1-mini context-conditioned molecule transfer V10.3 TDC-v2-HP — Oral bioavailability

Validation-selected checkpoint from the direct-inclusive TDC-v2-HP Oral bioavailability run. This was the best held-out macro-F1@5 result among the indirect-only and Starling-direct TDC variants.

Provenance

Base model: jiosephlee/Intern-S1-mini-lm
Base model revision: fcb667c380ae01f57693a45b4b5c2d331052a107
Training dataset: jiosephlee/context-conditioned-molecule-transfer-v10.3-tdc-v2-hp-bioavailability-ma-mixed-continuous-intern
Dataset revision: 9db99d12536f5dbb3d906ab90e5acff1b036405c
Training data: 49,224 direct records, including TDC and Starling direct data, plus 49,224 indirect records
Training configuration: 10 scheduled epochs, seed 42, soft-target loss, 8 GPUs, device batch size 4, gradient accumulation 4, Flash Attention 2, BFD packing, and padding-free training
Selection metric: validation knn_binary_macro_f1_at_5
Selected optimizer step: 80
Selected validation macro-F1@5: 0.6297
Training was intentionally stopped at step 202 of 440; this repository contains the saved step-80 validation-selected checkpoint.
Training run
Held-out model/Morgan comparison

Held-out test comparison

Ranker	Macro-F1@3	Macro-F1@5	Spearman@3	Top-1 hit@3
Model	0.5657	0.5657	0.5774	0.7266
Morgan vanilla	0.5987	0.5060	-0.0207	0.9297
Morgan weighted	0.6169	0.5439	-0.0142	0.9219

Checkpoint selection and the TDC-variant comparison use macro-F1@5. All model and Morgan test metrics are available in the linked W&B evaluation run.

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

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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-v2-hp-bioavailability-ma-best

Base model

jiosephlee/Intern-S1-mini-lm

Finetuned

(30)

this model

Intern-S1-mini context-conditioned molecule transfer V10.3 TDC-v2-HP — Oral bioavailability

Provenance

Held-out test comparison

Model tree for jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-bioavailability-ma-best

Dataset used to train jiosephlee/intern-s1-mini-context-conditioned-molecule-transfer-v10-3-tdc-v2-hp-bioavailability-ma-best