Text Generation
Transformers
Safetensors
Portuguese
qwen3
text-generation-inference
conversational
Eval Results (legacy)
Instructions to use Polygl0t/Tucano2-qwen-1.5B-Think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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" | |