Instructions to use BueormLLC/RAGPT-2_unfunctional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BueormLLC/RAGPT-2_unfunctional with Transformers:

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

pipe = pipeline("text-generation", model="BueormLLC/RAGPT-2_unfunctional")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BueormLLC/RAGPT-2_unfunctional")
model = AutoModelForCausalLM.from_pretrained("BueormLLC/RAGPT-2_unfunctional", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BueormLLC/RAGPT-2_unfunctional with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BueormLLC/RAGPT-2_unfunctional"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BueormLLC/RAGPT-2_unfunctional",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BueormLLC/RAGPT-2_unfunctional

SGLang

How to use BueormLLC/RAGPT-2_unfunctional 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 "BueormLLC/RAGPT-2_unfunctional" \
    --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": "BueormLLC/RAGPT-2_unfunctional",
		"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 "BueormLLC/RAGPT-2_unfunctional" \
        --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": "BueormLLC/RAGPT-2_unfunctional",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BueormLLC/RAGPT-2_unfunctional with Docker Model Runner:
```
docker model run hf.co/BueormLLC/RAGPT-2_unfunctional
```

Gerson Fabian Buenahora Ormaza commited on Jul 25, 2024

Commit

0e0be5a

verified ·

1 Parent(s): e6695af

Update README.md

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@@ -49,19 +49,18 @@ To use the model:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-model_name = "BueormLLC/RAGPT-2"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-# Prepare input
-context = "Your context here"
-question = "Your question here"
-input_text = f"Contexto: {context}\nPregunta: {question}\nRespuesta:"
-# Generate answer
 input_ids = tokenizer.encode(input_text, return_tensors="pt")
 output = model.generate(input_ids, max_length=150, num_return_sequences=1)
 answer = tokenizer.decode(output[0], skip_special_tokens=True)
 ```
 ## Limitations

 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("BueormLLC/RAGPT")
+model = AutoModelForCausalLM.from_pretrained("BueormLLC/RAGPT")
+context = "Mount Everest is the highest mountain in the world, with a height of 8,848 meters."
+question = "What is the height of Mount Everest?"
+input_text = f"Context: {context}\nquestion: {question}\nanswer:"
 input_ids = tokenizer.encode(input_text, return_tensors="pt")
 output = model.generate(input_ids, max_length=150, num_return_sequences=1)
 answer = tokenizer.decode(output[0], skip_special_tokens=True)
+print(f"Respuesta generada: {answer}")
 ```
 ## Limitations