Instructions to use TucanoBR/Tucano-1b1-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TucanoBR/Tucano-1b1-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="TucanoBR/Tucano-1b1-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TucanoBR/Tucano-1b1-Instruct")
model = AutoModelForCausalLM.from_pretrained("TucanoBR/Tucano-1b1-Instruct", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TucanoBR/Tucano-1b1-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TucanoBR/Tucano-1b1-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-1b1-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TucanoBR/Tucano-1b1-Instruct

SGLang

How to use TucanoBR/Tucano-1b1-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-1b1-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-1b1-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-1b1-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-1b1-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TucanoBR/Tucano-1b1-Instruct with Docker Model Runner:
```
docker model run hf.co/TucanoBR/Tucano-1b1-Instruct
```

nicholasKluge commited on Jan 15, 2025

Commit

9beabc7

verified ·

1 Parent(s): 5c0d0e1

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -25,9 +25,9 @@ widget:
 inference:
   parameters:
     repetition_penalty: 1.2
-    temperature: 0.2
-    top_k: 20
-    top_p: 0.2
     max_new_tokens: 150
 co2_eq_emissions:
   emissions: 21890
@@ -365,9 +365,9 @@ generation_config = GenerationConfig(
     "max_new_tokens": 2048,
     "renormalize_logits": True,
     "repetition_penalty": 1.2,
-    "temperature": 0.3,
-    "top_k": 30,
-    "top_p": 0.3,
     "use_cache": True,
   }
 )

 inference:
   parameters:
     repetition_penalty: 1.2
+    temperature: 0.1
+    top_k: 50
+    top_p: 1.0
     max_new_tokens: 150
 co2_eq_emissions:
   emissions: 21890
     "max_new_tokens": 2048,
     "renormalize_logits": True,
     "repetition_penalty": 1.2,
+    "temperature": 0.1,
+    "top_k": 50,
+    "top_p": 1.0,
     "use_cache": True,
   }
 )