Text Generation
Transformers
Safetensors
Portuguese
qwen3
text-generation-inference
conversational
Eval Results (legacy)
Instructions to use Polygl0t/Tucano2-qwen-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Polygl0t/Tucano2-qwen-0.5B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Polygl0t/Tucano2-qwen-0.5B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Polygl0t/Tucano2-qwen-0.5B-Instruct") model = AutoModelForCausalLM.from_pretrained("Polygl0t/Tucano2-qwen-0.5B-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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Polygl0t/Tucano2-qwen-0.5B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Polygl0t/Tucano2-qwen-0.5B-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": "Polygl0t/Tucano2-qwen-0.5B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Polygl0t/Tucano2-qwen-0.5B-Instruct
- SGLang
How to use Polygl0t/Tucano2-qwen-0.5B-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 "Polygl0t/Tucano2-qwen-0.5B-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": "Polygl0t/Tucano2-qwen-0.5B-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 "Polygl0t/Tucano2-qwen-0.5B-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": "Polygl0t/Tucano2-qwen-0.5B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Polygl0t/Tucano2-qwen-0.5B-Instruct with Docker Model Runner:
docker model run hf.co/Polygl0t/Tucano2-qwen-0.5B-Instruct
| model_name: Tucano2-qwen-0.5B-Instruct | |
| results: | |
| niah_pt_multikey_1_1024: 0.658 | |
| niah_pt_multikey_1_1024_stderr: 0.021236147199899316 | |
| niah_pt_multikey_1_2048: 0.556 | |
| niah_pt_multikey_1_2048_stderr: 0.022242244375731048 | |
| niah_pt_multikey_1_4096: 0.42 | |
| niah_pt_multikey_1_4096_stderr: N/A | |
| niah_pt_multikey_1_alias: " - niah_pt_multikey_1" | |
| niah_pt_multikey_2_1024: 0.596 | |
| niah_pt_multikey_2_1024_stderr: 0.021966635293832883 | |
| niah_pt_multikey_2_2048: 0.366 | |
| niah_pt_multikey_2_2048_stderr: 0.021564276850201684 | |
| niah_pt_multikey_2_4096: 0.184 | |
| niah_pt_multikey_2_4096_stderr: N/A | |
| niah_pt_multikey_2_alias: " - niah_pt_multikey_2" | |
| niah_pt_multikey_3_1024: 0.406 | |
| niah_pt_multikey_3_1024_stderr: 0.021983962090086417 | |
| niah_pt_multikey_3_2048: 0.11 | |
| niah_pt_multikey_3_2048_stderr: 0.01400686919941566 | |
| niah_pt_multikey_3_4096: 0.038 | |
| niah_pt_multikey_3_4096_stderr: N/A | |
| niah_pt_multikey_3_alias: " - niah_pt_multikey_3" | |
| niah_pt_multiquery_1024: 0.554 | |
| niah_pt_multiquery_1024_stderr: 0.014700346948313894 | |
| niah_pt_multiquery_2048: 0.4545 | |
| niah_pt_multiquery_2048_stderr: 0.014300997764986478 | |
| niah_pt_multiquery_4096: 0.395 | |
| niah_pt_multiquery_4096_stderr: N/A | |
| niah_pt_multiquery_alias: " - niah_pt_multiquery" | |
| niah_pt_multivalue_1024: 0.4885 | |
| niah_pt_multivalue_1024_stderr: 0.014608638699389432 | |
| niah_pt_multivalue_2048: 0.4675 | |
| niah_pt_multivalue_2048_stderr: 0.014090229563008424 | |
| niah_pt_multivalue_4096: 0.4145 | |
| niah_pt_multivalue_4096_stderr: N/A | |
| niah_pt_multivalue_alias: " - niah_pt_multivalue" | |
| niah_pt_single_1_1024: 0.602 | |
| niah_pt_single_1_1024_stderr: 0.021912377885779953 | |
| niah_pt_single_1_2048: 0.608 | |
| niah_pt_single_1_2048_stderr: 0.02185468495561119 | |
| niah_pt_single_1_4096: 0.522 | |
| niah_pt_single_1_4096_stderr: N/A | |
| niah_pt_single_1_alias: " - niah_pt_single_1" | |
| niah_pt_single_2_1024: 0.518 | |
| niah_pt_single_2_1024_stderr: 0.022368565117387874 | |
| niah_pt_single_2_2048: 0.4 | |
| niah_pt_single_2_2048_stderr: 0.02193084412072858 | |
| niah_pt_single_2_4096: 0.316 | |
| niah_pt_single_2_4096_stderr: N/A | |
| niah_pt_single_2_alias: " - niah_pt_single_2" | |
| niah_pt_single_3_1024: 0.63 | |
| niah_pt_single_3_1024_stderr: 0.021613289165165816 | |
| niah_pt_single_3_2048: 0.596 | |
| niah_pt_single_3_2048_stderr: 0.021966635293832883 | |
| niah_pt_single_3_4096: 0.522 | |
| niah_pt_single_3_4096_stderr: N/A | |
| niah_pt_single_3_alias: " - niah_pt_single_3" | |
| ruler_pt_4096: 0.38164545454545457 | |
| ruler_pt_4096_stderr: N/A | |
| ruler_pt_alias: ruler_pt | |
| ruler_pt_cwe_1024: 0.4992 | |
| ruler_pt_cwe_1024_stderr: 0.016325801161570425 | |
| ruler_pt_cwe_2048: 0.32839999999999997 | |
| ruler_pt_cwe_2048_stderr: 0.013636671059873462 | |
| ruler_pt_cwe_4096: 0.1778 | |
| ruler_pt_cwe_4096_stderr: N/A | |
| ruler_pt_cwe_alias: " - ruler_pt_cwe" | |
| ruler_pt_fwe_1024: 0.8353333333333334 | |
| ruler_pt_fwe_1024_stderr: 0.009076286695702566 | |
| ruler_pt_fwe_2048: 0.6906666666666667 | |
| ruler_pt_fwe_2048_stderr: 0.010496640893696112 | |
| ruler_pt_fwe_4096: 0.594 | |
| ruler_pt_fwe_4096_stderr: N/A | |
| ruler_pt_fwe_alias: " - ruler_pt_fwe" | |
| ruler_pt_vt_1024: 0.8847999999999999 | |
| ruler_pt_vt_1024_stderr: 0.009025566003490679 | |
| ruler_pt_vt_2048: 0.7112 | |
| ruler_pt_vt_2048_stderr: 0.013468181161820449 | |
| ruler_pt_vt_4096: 0.6147999999999999 | |
| ruler_pt_vt_4096_stderr: N/A | |
| ruler_pt_vt_alias: " - ruler_pt_vt" | |