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

Libraries

How to use LeroyDyer/SpydazWeb_AI_Swahili_Project with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/SpydazWeb_AI_Swahili_Project")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWeb_AI_Swahili_Project")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWeb_AI_Swahili_Project")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/SpydazWeb_AI_Swahili_Project with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LeroyDyer/SpydazWeb_AI_Swahili_Project

SGLang

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

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

UPDATED !!!## UPDATED !!!

this model has been updted for rag and tasks in swahili or english as well as prgramming and other school works stuff and bible ! as well as other sacred historical texts also !

This model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @

UPDATED !!!

undergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili

this is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: model.train() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..