Instructions to use stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B with Transformers:

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

pipe = pipeline("text-generation", model="stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B")
model = AutoModelForCausalLM.from_pretrained("stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B

SGLang

How to use stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B 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 "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B" \
    --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": "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B",
		"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 "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B" \
        --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": "stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B with Docker Model Runner:
```
docker model run hf.co/stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B
```

Model Card for Model ID

Translates sentences from French to Ghomala, native language of Bandjoun, a cameroonian village.

Example:

from transformers import AutoTokenizer, AutoModelForCausalLM

MAX_TOKENS = 256

tokenizer = AutoTokenizer.from_pretrained("stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B")
model = AutoModelForCausalLM.from_pretrained("stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B")

test_sentence = "bonjour Adam"
print(test_sentence)

system_prompt = """
1. You are a helpful specialist in linguistic, especially african language and you are required to provide the rightfull translation of a french expression into the ghomala language, the native language of bandjoun, a village of Cameroon.
2. Your ghomala translation should use correct phonetic signs.
"""

messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "Sentence (in french): <s>vieil homme</s>"},
        {"role": "assistant", "content": "Sentence (in ghomala): <s>bvo</s>"},
        {"role": "user", "content": f"Sentence (in french): <s>{test_sentence}</s>"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=MAX_TOKENS, tokenizer=tokenizer, do_sample=True, temperature=0.5, top_p=1, top_k=50, stop_strings=["Sentence (in french)", "</s>"], pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.batch_decode(outputs[:, inputs.shape[1]:])[0]
print(f'generated text: {generated_text}')

Model Details

Model Description

Developed by: Steve TUENO
License: llama3.2
Finetuned from model: meta-llama/Llama-3.2-1B

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

F32

Model tree for stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1751)

this model

stfotso
/

llama-3.2-tuned-french-ghomala-bandjoun-1B

Model Card for Model ID

Model Details

Model Description

Model tree for stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B

Datasets used to train stfotso/llama-3.2-tuned-french-ghomala-bandjoun-1B