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 10, 2023

Commit

108c3ae

1 Parent(s): e3f04e2

update README.md

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@@ -100,7 +100,7 @@ Use the code below to get started with the model.
 ```python
 import torch
-from peft import PeftConfig
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
@@ -158,7 +158,7 @@ print(response)
 ```python
 import torch
-from peft import PeftConfig
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"

 ```python
 import torch
+from peft import PeftConfig, PeftModel
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
 ```python
 import torch
+from peft import PeftConfig, PeftModel
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"