Instructions to use jiosephlee/assay-transfer-tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiosephlee/assay-transfer-tool with Transformers:

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

pipe = pipeline("text-generation", model="jiosephlee/assay-transfer-tool")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jiosephlee/assay-transfer-tool")
model = AutoModelForCausalLM.from_pretrained("jiosephlee/assay-transfer-tool", 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/assay-transfer-tool with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jiosephlee/assay-transfer-tool"
# 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/assay-transfer-tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jiosephlee/assay-transfer-tool

SGLang

How to use jiosephlee/assay-transfer-tool 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/assay-transfer-tool" \
    --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/assay-transfer-tool",
		"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/assay-transfer-tool" \
        --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/assay-transfer-tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jiosephlee/assay-transfer-tool with Docker Model Runner:
```
docker model run hf.co/jiosephlee/assay-transfer-tool
```

assay-transfer-tool

A full-parameter SFT of the Qwen3ForCausalLM Intern-S1-mini language backbone (jiosephlee/Intern-S1-mini-lm) for binary assay-transfer prediction on small molecules. Given a molecule (SMILES) and a paired-choice prompt, the model answers with (A) or (B).

The model uses the Intern SMILES-aware tokenizer, so load with trust_remote_code=True.

Training

Base model: jiosephlee/Intern-S1-mini-lm (Qwen3ForCausalLM, vocab 153216)
Dataset: jiosephlee/assay-transfer-intern (158,429 train rows)
Regime: full-parameter BF16, packing + padding-free, gradient checkpointing, Liger fused linear cross-entropy, paged_adamw_8bit
Chat template: enabled, thinking disabled; completions are (A) / (B)
LR: 2e-5 base, with SMILES input-embedding rows trained at 1.5x
Max length: 4096, 1 epoch

This checkpoint is the best-validation snapshot (step 50), promoted by the assay-transfer callback on binary macro-F1.

Validation metrics (source_value split, n=1703)

Metric	Value
Binary macro-F1	0.6612
Binary accuracy	0.7046
Parse rate	1.00
assay_concept group-avg macro-F1	0.6321
tanimoto_bucket group-avg macro-F1	0.6316

Per-assay macro-F1: Fa 0.532, Fg 0.593, Fh 0.704, oral_bioavailability 0.600, oral_exposure 0.732. Per-Tanimoto-bucket macro-F1: high 0.664, low 0.600.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("jiosephlee/assay-transfer-tool", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jiosephlee/assay-transfer-tool", torch_dtype="bfloat16")

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for jiosephlee/assay-transfer-tool

Base model

jiosephlee/Intern-S1-mini-lm

Finetuned

(9)

this model