Instructions to use EmergenceAI/RAG-response-generation-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmergenceAI/RAG-response-generation-model-v1 with Transformers:

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

pipe = pipeline("text-generation", model="EmergenceAI/RAG-response-generation-model-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmergenceAI/RAG-response-generation-model-v1")
model = AutoModelForCausalLM.from_pretrained("EmergenceAI/RAG-response-generation-model-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EmergenceAI/RAG-response-generation-model-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/EmergenceAI/RAG-response-generation-model-v1

SGLang

How to use EmergenceAI/RAG-response-generation-model-v1 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 "EmergenceAI/RAG-response-generation-model-v1" \
    --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": "EmergenceAI/RAG-response-generation-model-v1",
		"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 "EmergenceAI/RAG-response-generation-model-v1" \
        --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": "EmergenceAI/RAG-response-generation-model-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmergenceAI/RAG-response-generation-model-v1 with Docker Model Runner:
```
docker model run hf.co/EmergenceAI/RAG-response-generation-model-v1
```

aditya-emergence commited on Jun 23, 2024

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@@ -8,7 +8,7 @@ inference: false
 # Emergence-RAG-response-generation
-Emergence-RAG-response-generation is a 13b parameter decoder-style transformer model for RAG applications. It is fine-tuned from a llama2-13b base-model.
 This model was trained by [Emergence AI](https://www.emergence.ai/).

 # Emergence-RAG-response-generation
+Emergence-RAG-response-generation is a 13b parameter decoder-style transformer model for RAG applications. It is fine-tuned from a [llama2-13b](https://huggingface.co/meta-llama/Llama-2-13b-hf) base-model.
 This model was trained by [Emergence AI](https://www.emergence.ai/).