Instructions to use theophilusowiti/Caracal_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theophilusowiti/Caracal_instruct with Transformers:

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

pipe = pipeline("text-generation", model="theophilusowiti/Caracal_instruct", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("theophilusowiti/Caracal_instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("theophilusowiti/Caracal_instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theophilusowiti/Caracal_instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/theophilusowiti/Caracal_instruct

SGLang

How to use theophilusowiti/Caracal_instruct 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 "theophilusowiti/Caracal_instruct" \
    --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": "theophilusowiti/Caracal_instruct",
		"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 "theophilusowiti/Caracal_instruct" \
        --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": "theophilusowiti/Caracal_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use theophilusowiti/Caracal_instruct with Docker Model Runner:
```
docker model run hf.co/theophilusowiti/Caracal_instruct
```

theophilusowiti commited on Aug 8

Commit

10a6fad

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1 Parent(s): fc245b6

Update README.md

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@@ -98,6 +98,7 @@ If you use this model or its outputs in your research, please cite:
   institution  = {Carnegie Mellon University Africa},
   note         = {GPU compute provided by OpenToken and Leafcloud}
 }
 ## Usage

   institution  = {Carnegie Mellon University Africa},
   note         = {GPU compute provided by OpenToken and Leafcloud}
 }
+```
 ## Usage