jpacifico/French-Alpaca-dataset-Instruct-55K
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How to use mintujohnson/Llama-3.2-3B-French-Instruct with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="mintujohnson/Llama-3.2-3B-French-Instruct")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mintujohnson/Llama-3.2-3B-French-Instruct")
model = AutoModelForCausalLM.from_pretrained("mintujohnson/Llama-3.2-3B-French-Instruct")
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]:]))How to use mintujohnson/Llama-3.2-3B-French-Instruct with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mintujohnson/Llama-3.2-3B-French-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "mintujohnson/Llama-3.2-3B-French-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/mintujohnson/Llama-3.2-3B-French-Instruct
How to use mintujohnson/Llama-3.2-3B-French-Instruct with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "mintujohnson/Llama-3.2-3B-French-Instruct" \
--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": "mintujohnson/Llama-3.2-3B-French-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "mintujohnson/Llama-3.2-3B-French-Instruct" \
--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": "mintujohnson/Llama-3.2-3B-French-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use mintujohnson/Llama-3.2-3B-French-Instruct with Unsloth Studio:
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 mintujohnson/Llama-3.2-3B-French-Instruct to start chatting
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 mintujohnson/Llama-3.2-3B-French-Instruct to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mintujohnson/Llama-3.2-3B-French-Instruct to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="mintujohnson/Llama-3.2-3B-French-Instruct",
max_seq_length=2048,
)How to use mintujohnson/Llama-3.2-3B-French-Instruct with Docker Model Runner:
docker model run hf.co/mintujohnson/Llama-3.2-3B-French-Instruct
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
from unsloth import FastLanguageModel
from transformers import TextStreamer
model_path = "mintujohnson/Llama-3.2-3B-French-Instruct"
model, tokenizer = FastLanguageModel.from_pretrained(model_name = model_path, max_seq_length = 128,
dtype = None, load_in_4bit = True)
def inference(messages, model, tokenizer):
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer.apply_chat_template(
messages, tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
).to("cuda")
print(tokenizer.decode(inputs[0], skip_special_tokens=False))
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(
input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.5, min_p = 0.1)
messages = [
{"role": "user", "content": "où est la Normandie?"},
]
output = inference(messages, model, tokenizer)