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Model: PORTULAN/gervasio-8b-portuguese-ptpt-decoder Source: Original Platform
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README.md
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---
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license: mit
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language:
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- pt
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tags:
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- gervasio-pt*
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- gervasio-ptpt
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- gervasio-8b-portuguese-ptpt-decoder
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- portulan
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- albertina-pt*
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- serafim-pt*
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- clm
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- gpt
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- portuguese
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- decoder
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- foundation model
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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base_model_relation: finetune
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pipeline_tag: text-generation
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library_name: transformers
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---
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</br>
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</br>
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<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
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<p style="text-align: center;"> This is the model card for <b>Gervásio 8B PTPT</b> decoder.
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</br>
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This model is integrated in the <a href="https://evaristo.ai"><b>Evaristo.ai chatbot</b></a>, where its generative capabilities can be experimented with on the fly through a GUI.
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</br>
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You may be interested also in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Serafim (sentence encoder) families</a>.
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</p>
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</br>
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</br>
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<img width="500" src="logo_gervasio_long_color.png">
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</br>
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# Gervásio 8B PTPT
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</br>
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**Gervásio 8B PTPT** is an **open** decoder for the **Portuguese language**.
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It is a **decoder** of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA 3.1 8B Instruct model.
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Its further improvement through additional training was done over language resources that include data sets of Portuguese prepared for this purpose, that include [extraGLUE-Instruct
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](https://huggingface.co/datasets/PORTULAN/extraglue-instruct), as well as other data sets whose release is being prepared (MMLU PT, Natural Instructions PT, Wikipedia subset, Provérbios PT).
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**Gervásio 8B PTPT** is openly distributed for free under an open license, including thus for research and commercial purposes, and given its size, can be run on consumer-grade hardware.
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**Gervásio 8B PTPT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
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For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**,
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and which is known more shortly as **Gervásio PT*** or, even more briefly, just as **Gervásio**, among its acquaintances.
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**Gervásio 8B PTPT** is developed by a team from the University of Lisbon, Portugal.
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<br>
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<br>
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# Model Description
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The model has 8 billion parameters, over 32 layers, with a hidden size of 4096, an intermediate size of 14336, and 32 attention heads. It uses a RoPE tokenizer with a vocabulary of size 128256.
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<br>
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<br>
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# Training Data
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**Gervásio 8B PTPT** was trained on various datasets, either native to European Portuguese or translated into European Portuguese.
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For the latter, we selected only those datasets where the outcome of their translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.
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The training data comprises:
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- [extraGLUE-Instruct](https://huggingface.co/datasets/PORTULAN/extraglue-instruct)
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- MMLU PT (multiple choice question answering).
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- A subset of Natural Instructions (mostly multiple choice question answering tasks).
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- A manually curated subset of Wikipedia.
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- A manually curated list of proverbs.
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<br>
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<br>
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# Training Details
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We applied supervised fine-tuning with a causal language modeling training objective following a zero-out technique during the fine-tuning process. Specifically, while the entire prompt and chat template received attention during fine-tuning, only the response tokens were subjected to back-propagation.
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To accelerate training, the Fully Sharded Data Parallel (FSDP) paradigm was used over 10 L40S GPUs.
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<br>
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<br>
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# Performance
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For testing, we use translations of the standard benchmarks GPQA Diamond, MMLU and MMLU Pro, as well as the CoPA, MRPC and RTE datasets in [extraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue).
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| Model | GPQA Diamond PT | MMLU PT | MMLU Pro PT | CoPA | MRPC | RTE | Average |
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| ---------------------- | --------------: | --------: | ----------: | --------: | --------: | --------: | --------: |
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| Gervásio 8B PTPT | **34.85** | **62.15** | **36.79** | **87.00** | **77.45** | 77.62 | **62.64** |
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| LLaMA 3.1 8B Instruct | 32.32 | 61.49 | 36.10 | 83.00 | 75.25 | **79.42** | 61.26 |
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<br>
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<br>
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# How to use
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You can use this model directly with a pipeline for causal language modeling:
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```python3
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>>> from transformers import pipeline
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>>> generator = pipeline(model='PORTULAN/gervasio-8b-portuguese-ptpt-decoder')
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>>> generator("A comida portuguesa é", max_new_tokens=10)
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```
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<br>
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<br>
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# Chatbot
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This model is integrated in the **chatbot** [**Evaristo.ai**](https://evaristo.ai), where its generative capabilities can be experimented with on the fly through a GUI.
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<br>
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<br>
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# Please cite
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``` latex
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@misc{gervasio,
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title={Advancing Generative AI for Portuguese with
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Open Decoder Gervásio PT-*},
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author={Rodrigo Santos, João Silva, Luís Gomes,
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João Rodrigues, António Branco},
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year={2024},
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eprint={2402.18766},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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Please use the above canonical reference when using or citing this model.
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<br>
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<br>
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# Acknowledgments
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The research reported here was partially supported by:
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PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the grant PINFRA/22117/2016;
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innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação I.P. under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização;
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research project "Hey, Hal, curb your hallucination! / Enhancing AI chatbots with enhanced RAG solutions", funded by FCT-Fundação para a Ciência e a Tecnologia under the grant 2024.07592.IACDC;
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project "CLARIN – Infraestrutura de Investigação para a Ciência e Tecnologia da Linguagem", funded by programme Lisboa2030 under the grant LISBOA2030-FEDER-01316900PORTULAN.
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109
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
|
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
|
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{{- message.content | tojson }}
|
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{%- else %}
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{{- message.content }}
|
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{%- endif %}
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{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
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{%- if add_generation_prompt %}
|
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
|
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"LlamaForCausalLM"
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],
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
|
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"head_dim": 128,
|
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
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"factor": 8.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.3",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
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generation_config.json
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generation_config.json
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{
|
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"bos_token_id": 128000,
|
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"do_sample": true,
|
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"eos_token_id": [
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128001,
|
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128008,
|
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128009
|
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],
|
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"pad_token_id": 128009,
|
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"temperature": 0.6,
|
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"top_p": 0.9,
|
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"transformers_version": "4.52.3"
|
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}
|
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gervasio-8b-portuguese-ptpt-decoder-F16.gguf
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gervasio-8b-portuguese-ptpt-decoder-F16.gguf
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logo_gervasio_long_color.png
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version https://git-lfs.github.com/spec/v1
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model-00001-of-00004.safetensors
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user