Instructions to use Rocketknight1/tiny-random-falcon-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rocketknight1/tiny-random-falcon-7b with Transformers:

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

pipe = pipeline("text-generation", model="Rocketknight1/tiny-random-falcon-7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/tiny-random-falcon-7b")
model = AutoModelForCausalLM.from_pretrained("Rocketknight1/tiny-random-falcon-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rocketknight1/tiny-random-falcon-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rocketknight1/tiny-random-falcon-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rocketknight1/tiny-random-falcon-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rocketknight1/tiny-random-falcon-7b

SGLang

How to use Rocketknight1/tiny-random-falcon-7b 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 "Rocketknight1/tiny-random-falcon-7b" \
    --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": "Rocketknight1/tiny-random-falcon-7b",
		"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 "Rocketknight1/tiny-random-falcon-7b" \
        --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": "Rocketknight1/tiny-random-falcon-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rocketknight1/tiny-random-falcon-7b with Docker Model Runner:
```
docker model run hf.co/Rocketknight1/tiny-random-falcon-7b
```

tiny-random-falcon-7b

Commit History

Sep 6, 2023

16:34
Adding `safetensors` variant of this model⁠

SFconvertbot

ac13588

Jul 7, 2023

16:32
Upload tokenizer

Rocketknight1 HF Staff

d2373e7
16:27
Upload FalconForCausalLM

Rocketknight1 HF Staff

b11cf41

Jul 6, 2023

16:44
Update config.json

Rocketknight1 HF Staff

f9c0179

Jul 4, 2023

15:35
Upload tokenizer

Rocketknight1 HF Staff

953bcae
15:35
Upload FalconForCausalLM

Rocketknight1 HF Staff

41805d2
15:34
initial commit

Rocketknight1 HF Staff

01b72c3

Commit History

Sep 6, 2023

Adding `safetensors` variant of this model⁠

Jul 7, 2023

Upload tokenizer

Upload FalconForCausalLM

Jul 6, 2023

Update config.json

Jul 4, 2023

Upload tokenizer

Upload FalconForCausalLM

initial commit