Instructions to use Sandiago21/llama-7b-hf-prompt-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandiago21/llama-7b-hf-prompt-answering with Transformers:

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

pipe = pipeline("text-generation", model="Sandiago21/llama-7b-hf-prompt-answering")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering")
model = AutoModelForCausalLM.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering", device_map="auto")

PEFT
How to use Sandiago21/llama-7b-hf-prompt-answering with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sandiago21/llama-7b-hf-prompt-answering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandiago21/llama-7b-hf-prompt-answering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering

SGLang

How to use Sandiago21/llama-7b-hf-prompt-answering 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 "Sandiago21/llama-7b-hf-prompt-answering" \
    --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"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 "Sandiago21/llama-7b-hf-prompt-answering" \
        --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sandiago21/llama-7b-hf-prompt-answering with Docker Model Runner:
```
docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering
```

Sandiago21 commited on Jun 3, 2023

Commit

13d8a16

1 Parent(s): 1c1a961

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -3,7 +3,7 @@ license: other
 language:
 - en
 library_name: transformers
-pipeline_tag: conversational
 tags:
 - llama
 - decapoda-research-7b-hf
@@ -207,5 +207,4 @@ The decapoda-research/llama-7b-hf model was further trained and finetuned on que
 ## Model Architecture and Objective
-The model is based on decapoda-research/llama-7b-hf model and finetuned adapters on top of the main model on conversations and question answering data.

 language:
 - en
 library_name: transformers
+pipeline_tag: text-generation
 tags:
 - llama
 - decapoda-research-7b-hf
 ## Model Architecture and Objective
+The model is based on decapoda-research/llama-7b-hf model and finetuned adapters on top of the main model on conversations and question answering data.