Instructions to use Polygl0t/Tucano2-qwen-1.5B-Think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Polygl0t/Tucano2-qwen-1.5B-Think with Transformers:

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

pipe = pipeline("text-generation", model="Polygl0t/Tucano2-qwen-1.5B-Think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Polygl0t/Tucano2-qwen-1.5B-Think")
model = AutoModelForCausalLM.from_pretrained("Polygl0t/Tucano2-qwen-1.5B-Think", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Polygl0t/Tucano2-qwen-1.5B-Think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Polygl0t/Tucano2-qwen-1.5B-Think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Polygl0t/Tucano2-qwen-1.5B-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Polygl0t/Tucano2-qwen-1.5B-Think

SGLang

How to use Polygl0t/Tucano2-qwen-1.5B-Think 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 "Polygl0t/Tucano2-qwen-1.5B-Think" \
    --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": "Polygl0t/Tucano2-qwen-1.5B-Think",
		"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 "Polygl0t/Tucano2-qwen-1.5B-Think" \
        --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": "Polygl0t/Tucano2-qwen-1.5B-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Polygl0t/Tucano2-qwen-1.5B-Think with Docker Model Runner:
```
docker model run hf.co/Polygl0t/Tucano2-qwen-1.5B-Think
```

nicholasKluge commited on Feb 11

Commit

6c6f582

verified ·

1 Parent(s): 4070029

Upload ruler.yaml with huggingface_hub

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ruler.yaml +82 -0

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	@@ -0,0 +1,82 @@

+model_name: Tucano2-qwen-1.5B-Think
+results:
+  niah_pt_multikey_1_1024: 0.706
+  niah_pt_multikey_1_1024_stderr: 0.02039509548493655
+  niah_pt_multikey_1_2048: 0.67
+  niah_pt_multikey_1_2048_stderr: 0.021049612166134782
+  niah_pt_multikey_1_4096: 0.542
+  niah_pt_multikey_1_4096_stderr: N/A
+  niah_pt_multikey_1_alias: " - niah_pt_multikey_1"
+  niah_pt_multikey_2_1024: 0.48
+  niah_pt_multikey_2_1024_stderr: 0.022365160424231326
+  niah_pt_multikey_2_2048: 0.238
+  niah_pt_multikey_2_2048_stderr: 0.019064072958198387
+  niah_pt_multikey_2_4096: 0.062
+  niah_pt_multikey_2_4096_stderr: N/A
+  niah_pt_multikey_2_alias: " - niah_pt_multikey_2"
+  niah_pt_multikey_3_1024: 0.486
+  niah_pt_multikey_3_1024_stderr: 0.022374298166353144
+  niah_pt_multikey_3_2048: 0.31
+  niah_pt_multikey_3_2048_stderr: 0.020704041021724684
+  niah_pt_multikey_3_4096: 0.184
+  niah_pt_multikey_3_4096_stderr: N/A
+  niah_pt_multikey_3_alias: " - niah_pt_multikey_3"
+  niah_pt_multiquery_1024: 0.531
+  niah_pt_multiquery_1024_stderr: 0.013691344193015646
+  niah_pt_multiquery_2048: 0.4915
+  niah_pt_multiquery_2048_stderr: 0.014053487147395266
+  niah_pt_multiquery_4096: 0.4215
+  niah_pt_multiquery_4096_stderr: N/A
+  niah_pt_multiquery_alias: " - niah_pt_multiquery"
+  niah_pt_multivalue_1024: 0.4995
+  niah_pt_multivalue_1024_stderr: 0.013476376569794338
+  niah_pt_multivalue_2048: 0.519
+  niah_pt_multivalue_2048_stderr: 0.013327913059930505
+  niah_pt_multivalue_4096: 0.4545
+  niah_pt_multivalue_4096_stderr: N/A
+  niah_pt_multivalue_alias: " - niah_pt_multivalue"
+  niah_pt_single_1_1024: 0.82
+  niah_pt_single_1_1024_stderr: 0.017198592476314233
+  niah_pt_single_1_2048: 0.816
+  niah_pt_single_1_2048_stderr: 0.017346174781752842
+  niah_pt_single_1_4096: 0.8
+  niah_pt_single_1_4096_stderr: N/A
+  niah_pt_single_1_alias: " - niah_pt_single_1"
+  niah_pt_single_2_1024: 0.778
+  niah_pt_single_2_1024_stderr: 0.018604414758250098
+  niah_pt_single_2_2048: 0.772
+  niah_pt_single_2_2048_stderr: 0.018781306529363172
+  niah_pt_single_2_4096: 0.688
+  niah_pt_single_2_4096_stderr: N/A
+  niah_pt_single_2_alias: " - niah_pt_single_2"
+  niah_pt_single_3_1024: 0.468
+  niah_pt_single_3_1024_stderr: 0.022337186479044296
+  niah_pt_single_3_2048: 0.508
+  niah_pt_single_3_2048_stderr: 0.022380208834928014
+  niah_pt_single_3_4096: 0.5
+  niah_pt_single_3_4096_stderr: N/A
+  niah_pt_single_3_alias: " - niah_pt_single_3"
+  ruler_pt_4096: 0.44008484848484847
+  ruler_pt_4096_stderr: N/A
+  ruler_pt_alias: ruler_pt
+  ruler_pt_cwe_1024: 0.2516
+  ruler_pt_cwe_1024_stderr: 0.0065879953982022075
+  ruler_pt_cwe_2048: 0.10560000000000001
+  ruler_pt_cwe_2048_stderr: 0.0046626989526502875
+  ruler_pt_cwe_4096: 0.268
+  ruler_pt_cwe_4096_stderr: N/A
+  ruler_pt_cwe_alias: " - ruler_pt_cwe"
+  ruler_pt_fwe_1024: 0.7766666666666666
+  ruler_pt_fwe_1024_stderr: 0.010771818051204566
+  ruler_pt_fwe_2048: 0.644
+  ruler_pt_fwe_2048_stderr: 0.010544896116732008
+  ruler_pt_fwe_4096: 0.5413333333333332
+  ruler_pt_fwe_4096_stderr: N/A
+  ruler_pt_fwe_alias: " - ruler_pt_fwe"
+  ruler_pt_vt_1024: 0.8336
+  ruler_pt_vt_1024_stderr: 0.01194434656352784
+  ruler_pt_vt_2048: 0.4344
+  ruler_pt_vt_2048_stderr: 0.014185758756689964
+  ruler_pt_vt_4096: 0.37960000000000005
+  ruler_pt_vt_4096_stderr: N/A
+  ruler_pt_vt_alias: " - ruler_pt_vt"