Text Generation
Transformers
Safetensors
qwen3
chemistry
drug-discovery
admet
assay-transfer
smiles
conversational
text-generation-inference
Instructions to use jiosephlee/assay-transfer-tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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=256) 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
Best checkpoint from intern-s1-mini_assay-transfer-intern (step 157, source_value binary_macro_f1=0.6908, acc=0.7195)
Browse files- metric.json +32 -32
- model-00001-of-00004.safetensors +1 -1
- model-00002-of-00004.safetensors +1 -1
- model-00003-of-00004.safetensors +1 -1
- model-00004-of-00004.safetensors +1 -1
metric.json
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{
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"metric": 0.
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"metric_key": "eval/source_value/binary_macro_f1",
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"metrics": {
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"Fh": {
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"oral_bioavailability": {
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"binary_valid_n": 1000
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"metric": 0.6908340564197536,
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"metric_key": "eval/source_value/binary_macro_f1",
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"metrics": {
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"binary_accuracy": 0.7195,
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"binary_by_group": {
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"assay_concept": {
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"Fa": {
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"Fg": {
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"low": {
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"binary_group_avg": {
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"assay_concept": {
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