Instructions to use jbenbudd/ADPrLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jbenbudd/ADPrLlama with Transformers:

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

pipe = pipeline("text-generation", model="jbenbudd/ADPrLlama")
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("jbenbudd/ADPrLlama")
model = AutoModelForCausalLM.from_pretrained("jbenbudd/ADPrLlama", 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]:]))

PEFT
How to use jbenbudd/ADPrLlama with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jbenbudd/ADPrLlama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jbenbudd/ADPrLlama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jbenbudd/ADPrLlama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jbenbudd/ADPrLlama

SGLang

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

Docker Model Runner
How to use jbenbudd/ADPrLlama with Docker Model Runner:
```
docker model run hf.co/jbenbudd/ADPrLlama
```

jbenbudd commited on May 28

Commit

f32d72e

verified ·

1 Parent(s): aeea1bd

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README.md CHANGED Viewed

@@ -1,63 +1,16 @@
 ---
-library_name: peft
-license: other
 base_model: GreatCaptainNemo/ProLLaMA_Stage_1
-pipeline_tag: text-generation
 tags:
-- llama-factory
-- lora
-- generated_from_trainer
-model-index:
-- name: train_1_epoch_test
-  results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-# train_1_epoch_test
-This model is a fine-tuned version of [GreatCaptainNemo/ProLLaMA_Stage_1](https://huggingface.co/GreatCaptainNemo/ProLLaMA_Stage_1) on the adpr_train dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3777
-- Num Input Tokens Seen: 2691984
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 40
-- num_epochs: 1.0
-### Training results
-### Framework versions
-- PEFT 0.15.2
-- Transformers 4.52.4
-- Pytorch 2.6.0+cu124
-- Datasets 3.6.0
-- Tokenizers 0.21.1

 ---
 base_model: GreatCaptainNemo/ProLLaMA_Stage_1
 tags:
+  - protein
+  - ptm
+  - adp-ribosylation
+  - lora
+  - peft
+library_name: transformers
 ---
+# ADPr-LLaMA
+LoRA-fine-tuned `GreatCaptainNemo/ProLLaMA_Stage_1` for predicting ADP-ribosylation (ADPr) PTM sites from 21-residue peptide windows. Output format: `Sites=<R5,D12,...>`.
+This is a **training-only stub card**. Final metrics (ROC, accuracy, precision, recall, F1, confusion matrix) are filled in by the companion evaluation notebook after running on the held-out test set.