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

Libraries

How to use theophilusowiti/Caracal_GPT with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theophilusowiti/Caracal_GPT", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("theophilusowiti/Caracal_GPT", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use theophilusowiti/Caracal_GPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theophilusowiti/Caracal_GPT"
# 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_GPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/theophilusowiti/Caracal_GPT

SGLang

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

Docker Model Runner
How to use theophilusowiti/Caracal_GPT with Docker Model Runner:
```
docker model run hf.co/theophilusowiti/Caracal_GPT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Caracal_GPT

This model is a Continuous Pre-trained (CPT) model, adapted from lelapa/InkubaLM-0.4B on the custom dataset. In this new model we add different languages from Kenya, Uganda, South Africa, Western Africa, Somalia, Ethiopia; sub-saharan Africa.

Model description

Caracal GPT is a small causal model that can be used for fine-tuning tasks. It's goal is to be used by the represented language speaker for fine-tuning to a certain language task.

The Pre-trained LM was trained on 20+ African languages, introducing Kenyan lanugages and other languages not widely available in many datasets - Luo (luo), Kamba (kam) , Maasai (mas), Somalia (som), etc.

Intended uses & limitations

The model is to be used for fine-tuning on instruction set data for the given languages.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 4
loss: 1.8

Framework versions

Transformers 4.45.2
Pytorch 2.11.0+cu128
Datasets 2.21.0
Tokenizers 0.20.3

Downloads last month: 897

Safetensors

Model size

0.7B params

Tensor type

BF16

Model tree for theophilusowiti/Caracal_GPT

Base model

lelapa/InkubaLM-0.4B

Finetuned

(11)

this model

Finetunes

1 model

Quantizations

2 models