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)

# 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)

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
```

model card update

by Papizo - opened 16 days ago

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

+17

-28

Papizo

16 days ago

Proposed changes

Heading
Update Model Card with Top Performing Languages, added training dataset on the Model Description table, updated the expected output

Overview
This pull request updates the model card to highlight the top-performing languages, added training dataset on the Model Description table for the Caracal_instruct model. It adds a quick-reference section categorizing the best-supported languages by their respective African regions.

Changes made
Model Card : Added a "Top Performing Languages" section at the bottom of the file.

Content Additions: Included 8 East African languages, 4 West African languages, 1 Central African language (Lingala), and 1 Southern African language (Xhosa).

Added training dataset on the Model Description table (Muri Dataset)

Updated the expected output.

model card update4d7b7806

theophilusowiti

Owner 12 days ago

Updates done by Partner

Updated the model card as per requirements.
Updates merged and acknowledged.

theophilusowiti changed pull request status to merged 12 days ago

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