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

fc245b6

verified ·

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Update README.md

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@@ -73,6 +73,32 @@ The underlying small language model (SLM) backbone for this QAT process was [Ink
 ---
 ## Usage
 ```python
@@ -149,4 +175,4 @@ The following hyperparameters were used during training:
 - Pytorch 2.12.0+cu126
 - Datasets 4.8.5
 - Tokenizers 0.20.3
----

 ---
+## Authors & Acknowledgments
+### Author
+* **Theophilus Lincoln Owiti** — *Carnegie Mellon University Africa*
+### Acknowledgments
+We gratefully acknowledge **[OpenToken](https://opentoken.global)** and their infrastructure partner **[Leafcloud](https://www.leaf.cloud/)** for providing the GPU compute resources that made training this model possible.
+---
+## Citation
+If you use this model or its outputs in your research, please cite:
+```bibtex
+@misc{owiti2026caracal,
+  title        = {Caracal Instruct},
+  author       = {Owiti, Theophilus Lincoln},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  journal      = {Hugging Face Repository},
+  howpublished = {\url{[https://huggingface.co/theophilusowiti/Caracal_instruct](https://huggingface.co/theophilusowiti/Caracal_instruct)}},
+  institution  = {Carnegie Mellon University Africa},
+  note         = {GPU compute provided by OpenToken and Leafcloud}
+}
 ## Usage
 ```python
 - Pytorch 2.12.0+cu126
 - Datasets 4.8.5
 - Tokenizers 0.20.3
+---