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README.md CHANGED
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  ---
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- license: apache-2.0
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- library_name: peft
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  tags:
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- - trl
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- - sft
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  - SFT
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  - WeniGPT
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- - generated_from_trainer
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  base_model: mistralai/Mistral-7B-Instruct-v0.2
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- datasets:
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- - generator
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  model-index:
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- - name: WeniGPT-Agents-Mistral-1.0.12-SFT
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  results: []
 
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # WeniGPT-Agents-Mistral-1.0.12-SFT
 
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- This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1614
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- ## Model description
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- More information needed
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- ## Intended uses & limitations
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- More information needed
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- ## Training and evaluation data
 
 
 
 
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- More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Training procedure
 
 
 
 
 
 
 
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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- - train_batch_size: 1
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- - eval_batch_size: 1
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- - seed: 42
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- - distributed_type: multi-GPU
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- - num_devices: 4
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  - gradient_accumulation_steps: 2
 
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  - total_train_batch_size: 8
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- - total_eval_batch_size: 4
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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- - lr_scheduler_warmup_ratio: 0.03
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- - training_steps: 330
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:------:|:----:|:---------------:|
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- | 0.8828 | 0.5357 | 30 | 1.1399 |
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- | 0.6609 | 1.0714 | 60 | 1.1470 |
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- | 0.3918 | 1.6071 | 90 | 1.1614 |
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- | 0.1679 | 2.1429 | 120 | 1.2335 |
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- | 0.1361 | 2.6786 | 150 | 1.2612 |
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- | 0.0571 | 3.2143 | 180 | 1.3479 |
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- | 0.0573 | 3.75 | 210 | 1.3919 |
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- | 0.0185 | 4.2857 | 240 | 1.5194 |
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- | 0.019 | 4.8214 | 270 | 1.5014 |
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- | 0.008 | 5.3571 | 300 | 1.5365 |
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- | 0.0086 | 5.8929 | 330 | 1.5505 |
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-
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-
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  ### Framework versions
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- - PEFT 0.10.0
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- - Transformers 4.40.0
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- - Pytorch 2.1.0+cu118
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- - Datasets 2.18.0
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- - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: mit
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+ library_name: "trl"
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  tags:
 
 
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  - SFT
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  - WeniGPT
 
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  base_model: mistralai/Mistral-7B-Instruct-v0.2
 
 
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  model-index:
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+ - name: Weni/WeniGPT-Agents-Mistral-1.0.12-SFT
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  results: []
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+ language: ['pt']
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  ---
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+ # Weni/WeniGPT-Agents-Mistral-1.0.12-SFT
 
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+ This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2] on the dataset Weni/wenigpt-agent-1.4.0 with the SFT trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
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+ Description: Experiment with SFT and a new tokenizer configuration for chat template of mistral
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  It achieves the following results on the evaluation set:
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+ {'eval_loss': 1.1614402532577515, 'eval_runtime': 38.6736, 'eval_samples_per_second': 1.189, 'eval_steps_per_second': 0.31, 'epoch': 5.892857142857143}
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+ ## Intended uses & limitations
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+ This model has not been trained to avoid specific intructions.
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+ ## Training procedure
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+ Finetuning was done on the model mistralai/Mistral-7B-Instruct-v0.2 with the following prompt:
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+ ```
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+ ---------------------
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+ System_prompt:
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+ Agora você se chama {name}, você é {occupation} e seu objetivo é {chatbot_goal}. O adjetivo que mais define a sua personalidade é {adjective} e você se comporta da seguinte forma:
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+ {instructions_formatted}
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+ {context_statement}
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+
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+ Lista de requisitos:
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+ - Responda de forma natural, mas nunca fale sobre um assunto fora do contexto.
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+ - Nunca traga informações do seu próprio conhecimento.
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+ - Repito é crucial que você responda usando apenas informações do contexto.
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+ - Nunca mencione o contexto fornecido.
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+ - Nunca mencione a pergunta fornecida.
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+ - Gere a resposta mais útil possível para a pergunta usando informações do conexto acima.
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+ - Nunca elabore sobre o porque e como você fez a tarefa, apenas responda.
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+
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+
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+ ---------------------
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+ Question:
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+ {question}
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+
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+ ---------------------
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+ Response:
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+ {answer}
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+
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+
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+ ---------------------
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+
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+ ```
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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+ - per_device_train_batch_size: 1
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+ - per_device_eval_batch_size: 1
 
 
 
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  - gradient_accumulation_steps: 2
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+ - num_gpus: 4
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  - total_train_batch_size: 8
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+ - optimizer: AdamW
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+ - lr_scheduler_type: cosine
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+ - num_steps: 330
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+ - quantization_type: bitsandbytes
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+ - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 256\n - lora_alpha: 128\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\n - task_type: CAUSAL_LM",)
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  ### Training results
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  ### Framework versions
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+ - transformers==4.40.0
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+ - datasets==2.18.0
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+ - peft==0.10.0
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+ - safetensors==0.4.2
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+ - evaluate==0.4.1
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+ - bitsandbytes==0.43
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+ - huggingface_hub==0.22.2
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+ - seqeval==1.2.2
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+ - auto-gptq==0.7.1
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+ - gpustat==1.1.1
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+ - deepspeed==0.14.0
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+ - wandb==0.16.6
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+ - trl==0.8.1
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+ - accelerate==0.29.3
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+ - coloredlogs==15.0.1
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+ - traitlets==5.14.2
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+ - git+https://github.com/casper-hansen/AutoAWQ.git
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+
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+ ### Hardware
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+ - Cloud provided: runpod.io
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