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README.md
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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tags: []
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---
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# Sabiá-3 (Qwen-2) tokenizer v2
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This new version improves the chat template by handling custom system prompts and including the new `tool` role.
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## Simple usage
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```python
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messages = [
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{"role": "system", "content": "Você é um assistante inteligente."},
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{"role": "user", "content": "Olá!"},
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{"role": "assistant", "content": "Olá! Como posso ajudar você hoje?"},
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{"role": "user", "content": "Qual é a capital do Brasil?"},
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]
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print(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True))
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```
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Will result in the following string:
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```
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Você é um assistante inteligente. USUÁRIO: Olá! ASSISTENTE: Olá! Como posso ajudar você hoje?<|endoftext|>USUÁRIO: Qual é a capital do Brasil? ASSISTENTE:
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```
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## Tools
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```python
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messages = [
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{"role": "system", "content": """You are a helpful customer support assistant. Use the supplied tools to assist the user.
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Uso de funções: Opcional
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Lista de funções disponíveis:
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<functions>
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[{"type": "function", "function": {"name": "get_delivery_date", "description": "Get the delivery date for a customer's order. Call this whenever you need to know the delivery date, for example when a customer asks 'Where is my package'", "parameters": {"type": "object", "properties": {"order_id": {"type": "string", "description": "The customer's order ID."}}, "required": ["order_id"], "additionalProperties": false}}}]
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</functions>
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Formato esperado de resposta quando houver utilização de funções:
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<multiplefunctions>
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<functioncall> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </functioncall>
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<functioncall> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </functioncall>
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...
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</multiplefunctions>
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Aspectos importantes:
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- Chamadas de funções DEVEM seguir o formato especificado
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- Parâmetros necessários DEVEM ser especificados"""},
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{"role": "user", "content": "Hi, can you tell me the delivery date for my order?"},
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{"role": "assistant", "content": "Hi there! I can help with that. Can you please provide your order ID?"},
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{"role": "user", "content": "i think it is order_12345"},
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{
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"role": "assistant",
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"tool_calls": [
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{
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"id": "call_62136354",
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"type": "function",
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"function": {
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"arguments": "{'order_id': 'order_12345'}",
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"name": "get_delivery_date"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": json.dumps({
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"order_id": "order_12345",
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"delivery_date": "00-00-2000"
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}),
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"tool_call_id": "call_62136354"
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}
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]
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```
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The resulting string will be:
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```
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You are a helpful customer support assistant. Use the supplied tools to assist the user.
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Uso de funções: Opcional
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Lista de funções disponíveis:
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<functions>
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[{"type": "function", "function": {"name": "get_delivery_date", "description": "Get the delivery date for a customer's order. Call this whenever you need to know the delivery date, for example when a customer asks 'Where is my package'", "parameters": {"type": "object", "properties": {"order_id": {"type": "string", "description": "The customer's order ID."}}, "required": ["order_id"], "additionalProperties": false}}}]
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</functions>
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Formato esperado de resposta quando houver utilização de funções:
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<multiplefunctions>
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<functioncall> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </functioncall>
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<functioncall> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </functioncall>
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...
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</multiplefunctions>
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Aspectos importantes:
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- Chamadas de funções DEVEM seguir o formato especificado
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- Parâmetros necessários DEVEM ser especificados USUÁRIO: Hi, can you tell me the delivery date for my order? ASSISTENTE: Hi there! I can help with that. Can you please provide your order ID?<|endoftext|>USUÁRIO: i think it is order_12345 ASSISTENTE: <multiplefunctions>
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<functioncall> {"name": "get_delivery_date", "arguments": {'order_id': 'order_12345'}} </functioncall>
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</multiplefunctions><|endoftext|>FUNCTION_RESPONSE: {"order_id": "order_12345", "delivery_date": "00-00-2000"} ASSISTENTE:
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```
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