Instructions to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b

SGLang

How to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b 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/Mixtral_AI_MiniTron_Swahili_3.75b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Mixtral_AI_MiniTron_Swahili_3.75b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b",
    max_seq_length=2048,
)

Docker Model Runner
How to use LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: LeroyDyer
License: apache-2.0
Finetuned from model : LeroyDyer/Mixtral_AI_MiniTron_II

This is a smaller model easier for fine tuning !! (faster) This model was created from a fresh untrained model and has only been trained with swahili : it is still training!

Plus it will run and train on the laptop no problem ! (only with text corpuses the context needs to be low as it will force the gpu to consume memory so small articles only; later after intensive training the context can be re-extended etc: ) 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 @

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))..

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 14

Safetensors

Model size

4B params

Tensor type

F16

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Quantizations

1 model

Dataset used to train LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b

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