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

Libraries

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

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

pipe = pipeline("text-generation", model="Sandiago21/falcon-7b-prompt-answering", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Sandiago21/falcon-7b-prompt-answering", trust_remote_code=True, dtype="auto")

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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandiago21/falcon-7b-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/falcon-7b-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

Sandiago21

librarian-bot commited on Sep 19, 2023

Commit

cc4eee0

1 Parent(s): f9d043a

Librarian Bot: Add base_model information to model (#1)

Browse files

- Librarian Bot: Add base_model information to model (ffc2da5e298be1625e10357ff2c08503e5c99de4)

Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -1,14 +1,15 @@
 ---
-license: other
 language:
 - en
 library_name: transformers
-pipeline_tag: text-generation
 tags:
 - falcon
 - falcon-7b
 - prompt answering
 - peft
 ---
 ## Model Card for Model ID

 ---
 language:
 - en
+license: other
 library_name: transformers
 tags:
 - falcon
 - falcon-7b
 - prompt answering
 - peft
+pipeline_tag: text-generation
+base_model: tiiuae/falcon-7b
 ---
 ## Model Card for Model ID