Instructions to use contributor-anonymous/Mol2Pro-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use contributor-anonymous/Mol2Pro-base with Transformers:

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

pipe = pipeline("text-generation", model="contributor-anonymous/Mol2Pro-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("contributor-anonymous/Mol2Pro-base")
model = AutoModelForSeq2SeqLM.from_pretrained("contributor-anonymous/Mol2Pro-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use contributor-anonymous/Mol2Pro-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "contributor-anonymous/Mol2Pro-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contributor-anonymous/Mol2Pro-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/contributor-anonymous/Mol2Pro-base

SGLang

How to use contributor-anonymous/Mol2Pro-base 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 "contributor-anonymous/Mol2Pro-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contributor-anonymous/Mol2Pro-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "contributor-anonymous/Mol2Pro-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contributor-anonymous/Mol2Pro-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use contributor-anonymous/Mol2Pro-base with Docker Model Runner:
```
docker model run hf.co/contributor-anonymous/Mol2Pro-base
```

contributor-anonymous commited on 7 days ago

Commit

f4c651c

verified ·

1 Parent(s): a736e53

Initial commit

Browse files

Files changed (4) hide show

README.md +50 -0
config.json +31 -0
generation_config.json +7 -0
pytorch_model.bin +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,50 @@

+---
+language: en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- t5
+- molecule-to-protein
+- smiles
+- protein-generation
+- binder
+- ligand
+license: apache-2.0
+datasets:
+- contributor-anonymous/Mol2Pro-Binder-Dataset
+---
+# Mol2Pro-base
+## Model description
+- **Architecture:** T5-efficient-base https://huggingface.co/google/t5-efficient-base
+- **Tokenization:** https://huggingface.co/contributor-anonymous/Mol2Pro-tokenizer
+- **Code:** https://github.com/contributor-anonymous/Mol2Pro-tools
+- **Training data** https://huggingface.co/datasets/contributor-anonymous/Mol2Pro-Binder-Dataset
+## How to use
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+model_id = "contributor-anonymous/Mol2Pro-base"
+tokenizer_id = "contributor-anonymous/Mol2Pro-tokenizer"
+# Load tokenizers
+tokenizer_mol = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="smiles")
+tokenizer_aa  = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="aa")
+# Load model
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+```
+## Intended use
+Research use only. The model generates candidate sequences conditioned on small-molecule inputs; it does not guarantee binding or function and must be validated experimentally.

config.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "_name_or_path": "google/t5-efficient-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "vocab_size": 1069
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.49.0"
+}

pytorch_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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