Instructions to use Britania/m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Britania/m2 with Transformers:

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

pipe = pipeline("text-generation", model="Britania/m2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Britania/m2")
model = AutoModelForCausalLM.from_pretrained("Britania/m2", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Britania/m2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Britania/m2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Britania/m2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Britania/m2

SGLang

How to use Britania/m2 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 "Britania/m2" \
    --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": "Britania/m2",
		"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 "Britania/m2" \
        --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": "Britania/m2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Britania/m2 with Docker Model Runner:
```
docker model run hf.co/Britania/m2
```

Britania commited on Apr 26, 2024

Commit

d1b01f0

verified ·

1 Parent(s): d462649

Update handler.py

Browse files

Files changed (1) hide show

handler.py +9 -1

handler.py CHANGED Viewed

@@ -3,6 +3,12 @@ from typing import Dict, Any, List
 import torch
 import json
 class EndpointHandler():
     def __init__(self, path=""):
         # Carga el modelo y el tokenizer
@@ -17,7 +23,9 @@ class EndpointHandler():
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         # Procesa la entrada y llama al modelo para generar texto
-        input_data = data
         template = """
         [INST] <<SYS>>
         A continuación se presentan parámetros fisiológicos y ambientales capturados por los sensores de un smartwatch utilizado por un atleta. Basándote en estos datos y en tu conocimiento de las rutinas de entrenamiento para atletas, crea una rutina diaria de entrenamiento personalizada para el atleta.

 import torch
 import json
+def model_inference(model, tokenizer, text, max_new_tokens, temperature):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.inference_mode():
+        outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=temperature)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 class EndpointHandler():
     def __init__(self, path=""):
         # Carga el modelo y el tokenizer
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         # Procesa la entrada y llama al modelo para generar texto
+        input_data = {}
+        for key, value in data.items():
+            input_data[key] = value
         template = """
         [INST] <<SYS>>
         A continuación se presentan parámetros fisiológicos y ambientales capturados por los sensores de un smartwatch utilizado por un atleta. Basándote en estos datos y en tu conocimiento de las rutinas de entrenamiento para atletas, crea una rutina diaria de entrenamiento personalizada para el atleta.