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README.md

@@ -1,70 +1,86 @@
 ---
 license: mit
-library_name: peft
+library_name: "trl"
 tags:
-- trl
-- dpo
-- generated_from_trainer
-base_model: HuggingFaceH4/zephyr-7b-beta
+- DPO
+- WeniGPT
+base_model: Weni/WeniGPT-2.3.3-Zephyr-7B-zephyr-prompt-LLM_Base_2.0.3_SFT_reduction_variation
 model-index:
-- name: WeniGPT-DPO-test
+- name: Weni/WeniGPT-DPO-test
   results: []
+language: ['pt']
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Weni/WeniGPT-DPO-test
 
-# WeniGPT-DPO-test
+This model is a fine-tuned version of [Weni/WeniGPT-2.3.3-Zephyr-7B-zephyr-prompt-LLM_Base_2.0.3_SFT_reduction_variation] on the dataset Weni/WeniGPT-QA-1.0.1_DPO with the DPO trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
 
-This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6931
-- Rewards/chosen: 0.0
-- Rewards/rejected: 0.0
-- Rewards/accuracies: 0.0
-- Rewards/margins: 0.0
-- Logps/rejected: -12.7530
-- Logps/chosen: -7.2418
-- Logits/rejected: -2.2130
-- Logits/chosen: -2.2118
+{'eval_loss': 0.6931471228599548, 'eval_runtime': 214.1053, 'eval_samples_per_second': 1.485, 'eval_steps_per_second': 0.743, 'eval_rewards/chosen': 0.0, 'eval_rewards/rejected': 0.0, 'eval_rewards/accuracies': 0.0, 'eval_rewards/margins': 0.0, 'eval_logps/rejected': -12.752974510192871, 'eval_logps/chosen': -7.2417826652526855, 'eval_logits/rejected': -2.2130377292633057, 'eval_logits/chosen': -2.2118029594421387, 'epoch': 0.0}
 
-## Model description
+## Intended uses & limitations
 
-More information needed
+This model has not been trained to avoid specific intructions. 
 
-## Intended uses & limitations
+## Training procedure
 
-More information needed
+Finetuning was done on the model Weni/WeniGPT-2.3.3-Zephyr-7B-zephyr-prompt-LLM_Base_2.0.3_SFT_reduction_variation with the following prompt:
 
-## Training and evaluation data
+```
++---------------------
+Pt:
+### Instruction:
+Você é um médico tratando um paciente com amnésia. Para responder as perguntas do paciente, você irá ler um texto anteriormente para se contextualizar. Se você trouxer informações desconhecidas, fora do texto lido, poderá deixar o paciente confuso. Se o paciente fizer uma questão sobre informações não presentes no texto, você precisa responder de forma educada que você não tem informação suficiente para responder, pois se tentar responder, pode trazer informações que não ajudarão o paciente recuperar sua memória.Lembre, se não estiver no texto, você precisa responder de forma educada que você não tem informação suficiente para responder. Precisamos ajudar o paciente.
+</s>### Input:
+TEXTO: {context}
 
-More information needed
+PERGUNTA: {question}
+</s>
+### Response:
+RESPOSTA: {chosen_response}</s>
+### Response:
+RESPOSTA: {rejected_response}</s>
+---------------------
 
-## Training procedure
+```
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
+- per_device_train_batch_size: 2
+- per_device_eval_batch_size: 2
 - gradient_accumulation_steps: 2
+- num_gpus: 1
 - total_train_batch_size: 4
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- training_steps: 1
-- mixed_precision_training: Native AMP
+- optimizer: AdamW
+- lr_scheduler_type: cosine
+- num_steps: 1
+- quantization_type: bitsandbytes
+- LoRA: ("\n  - bits: 4\n  - use_exllama: True\n  - device_map: auto\n  - use_cache: False\n  - lora_r: 8\n  - lora_alpha: 16\n  - lora_dropout: 0.1\n  - bias: none\n  - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n  - task_type: CAUSAL_LM",)
 
 ### Training results
 
-
-
 ### Framework versions
 
-- PEFT 0.8.2
-- Transformers 4.38.2
-- Pytorch 2.1.0+cu118
-- Datasets 2.17.1
-- Tokenizers 0.15.1
+- transformers==4.38.2
+- datasets==2.17.1
+- peft==0.8.2
+- safetensors==0.4.2
+- evaluate==0.4.1
+- bitsandbytes==0.42
+- huggingface_hub==0.20.3
+- seqeval==1.2.2
+- optimum==1.17.1
+- auto-gptq==0.7.0
+- gpustat==1.1.1
+- deepspeed==0.13.2
+- wandb==0.16.3
+- trl==0.7.11
+- accelerate==0.27.2
+- coloredlogs==15.0.1
+- traitlets==5.14.1
+- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
+
+### Hardware
+- Cloud provided: runpod.io