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
Upload evals.yaml with huggingface_hub
Browse files- evals.yaml +3 -0
evals.yaml
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hellaswag_poly_pt_acc_norm_stderr: 0.005200030264123482
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hellaswag_poly_pt_acc_stderr: 0.005056141839024339
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hellaswag_poly_pt_alias: hellaswag_poly_pt
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ifeval_pt_alias: ifeval_pt
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ifeval_pt_inst_level_loose_acc: 0.4186046511627907
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ifeval_pt_inst_level_loose_acc_stderr: N/A
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hellaswag_poly_pt_acc_norm_stderr: 0.005200030264123482
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hellaswag_poly_pt_acc_stderr: 0.005056141839024339
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hellaswag_poly_pt_alias: hellaswag_poly_pt
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humaneval_instruct_alias: humaneval_instruct
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humaneval_instruct_pass@1,create_test: 0.10365853658536585
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humaneval_instruct_pass@1_stderr,create_test: 0.023875115311878508
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ifeval_pt_alias: ifeval_pt
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ifeval_pt_inst_level_loose_acc: 0.4186046511627907
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ifeval_pt_inst_level_loose_acc_stderr: N/A
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