Instructions to use Hawoly18/llama3.2-3B-Wolof with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hawoly18/llama3.2-3B-Wolof with Transformers:

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

pipe = pipeline("text-generation", model="Hawoly18/llama3.2-3B-Wolof")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hawoly18/llama3.2-3B-Wolof")
model = AutoModelForCausalLM.from_pretrained("Hawoly18/llama3.2-3B-Wolof")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Hawoly18/llama3.2-3B-Wolof with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hawoly18/llama3.2-3B-Wolof"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hawoly18/llama3.2-3B-Wolof",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hawoly18/llama3.2-3B-Wolof

SGLang

How to use Hawoly18/llama3.2-3B-Wolof 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 "Hawoly18/llama3.2-3B-Wolof" \
    --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": "Hawoly18/llama3.2-3B-Wolof",
		"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 "Hawoly18/llama3.2-3B-Wolof" \
        --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": "Hawoly18/llama3.2-3B-Wolof",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hawoly18/llama3.2-3B-Wolof with Docker Model Runner:
```
docker model run hf.co/Hawoly18/llama3.2-3B-Wolof
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

outputs

This model is a fine-tuned version of meta-llama/Llama-3.2-3B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6534

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
2.4702	0.0556	25	2.5017
2.1788	0.1111	50	2.0390
1.8193	0.1667	75	1.8122
1.5859	0.2222	100	1.6534

Framework versions

Transformers 4.45.2
Pytorch 2.1.0+cu118
Datasets 3.0.1
Tokenizers 0.20.1

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

F32

Model tree for Hawoly18/llama3.2-3B-Wolof

Base model

meta-llama/Llama-3.2-3B

Finetuned

(447)

this model

Quantizations

1 model

Hawoly18
/

llama3.2-3B-Wolof

outputs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Hawoly18/llama3.2-3B-Wolof

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