Instructions to use spitfire4794/Alo-70m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spitfire4794/Alo-70m with Transformers:

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

pipe = pipeline("text-generation", model="spitfire4794/Alo-70m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("spitfire4794/Alo-70m")
model = AutoModelForCausalLM.from_pretrained("spitfire4794/Alo-70m")
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 Settings

vLLM

How to use spitfire4794/Alo-70m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spitfire4794/Alo-70m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "spitfire4794/Alo-70m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/spitfire4794/Alo-70m

SGLang

How to use spitfire4794/Alo-70m 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 "spitfire4794/Alo-70m" \
    --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": "spitfire4794/Alo-70m",
		"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 "spitfire4794/Alo-70m" \
        --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": "spitfire4794/Alo-70m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use spitfire4794/Alo-70m with Docker Model Runner:
```
docker model run hf.co/spitfire4794/Alo-70m
```

spitfire4794 commited on 5 days ago

Commit

d494ea2

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1 Parent(s): 5f6b6a9

Update README.md

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@@ -46,10 +46,9 @@ Alo-70M was trained using the ChatML template. The chat template is built direct
 from transformers import AutoTokenizer, AutoModelForCausalLM
 model_id = "spitfire4794/Alo-70M"
-tokenizer_id = "spitfire4794/beng_bpe"
 # Load the custom Bengali BPE tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
 # Define the instruction in ChatML format

 from transformers import AutoTokenizer, AutoModelForCausalLM
 model_id = "spitfire4794/Alo-70M"
 # Load the custom Bengali BPE tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
 # Define the instruction in ChatML format