Instructions to use TucanoBR/Tucano-2b4-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TucanoBR/Tucano-2b4-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="TucanoBR/Tucano-2b4-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TucanoBR/Tucano-2b4-Instruct")
model = AutoModelForCausalLM.from_pretrained("TucanoBR/Tucano-2b4-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TucanoBR/Tucano-2b4-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TucanoBR/Tucano-2b4-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": "TucanoBR/Tucano-2b4-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TucanoBR/Tucano-2b4-Instruct

SGLang

How to use TucanoBR/Tucano-2b4-Instruct 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 "TucanoBR/Tucano-2b4-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": "TucanoBR/Tucano-2b4-Instruct",
		"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 "TucanoBR/Tucano-2b4-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": "TucanoBR/Tucano-2b4-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TucanoBR/Tucano-2b4-Instruct with Docker Model Runner:
```
docker model run hf.co/TucanoBR/Tucano-2b4-Instruct
```

nicholasKluge commited on Nov 9, 2024

Commit

e41c8c0

verified ·

1 Parent(s): d16e60c

Upload emissions-2b4-instruct.csv with huggingface_hub

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emissions-2b4-instruct.csv +3 -0

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	@@ -0,0 +1,3 @@

+timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue,n_nodes
+2024-11-06T19:20:52,Tucano-sft,5f0d1e08-8524-40ab-adeb-92401acb0afa,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,31672.27382905176,9.94885293271022,0.0003141186826815232,112.5,1402.2599449871632,1462.5000000000002,0.9897571840361336,12.270581318401753,12.855563670314986,26.115902172752907,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34,3.11.3,2.7.1,512,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.1178,50.7246,3900,machine,N,1.0,4
+2024-11-09T17:17:45,Tucano-dpo,6121fbd2-bea2-4942-8000-ac07bf388c86,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,12777.643057700712,2.476683906212117,0.0001938294797427051,112.5,351.3174231090531,365.62500000000006,0.39930072949185036,4.805598554475765,1.2964366031884842,6.501335887156102,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34,3.11.3,2.7.2,128,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.1178,50.7246,975,machine,N,1.0,1