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README.md

@@ -1,74 +1,103 @@
 ---
-license: apache-2.0
-library_name: peft
+license: mit
+library_name: "trl"
 tags:
-- trl
-- kto
 - KTO
 - WeniGPT
-- generated_from_trainer
 base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
 model-index:
-- name: WeniGPT-Agents-Mixstral-Instruct-2.0.0-KTO
+- name: Weni/WeniGPT-Agents-Mixstral-Instruct-2.0.0-KTO
   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-Agents-Mixstral-Instruct-2.0.0-KTO
 
-# WeniGPT-Agents-Mixstral-Instruct-2.0.0-KTO
+This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1] on the dataset Weni/wenigpt-agent-1.2.0 with the KTO trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
+Description: KTO with Agents 1.2.0 dataset and Mixstral model
 
-This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3856
-- Rewards/chosen: -2.7129
-- Logps/chosen: -263.7560
-- Rewards/rejected: -6.5728
-- Logps/rejected: -282.9854
-- Kl: 0.0
-- Rewards/margins: 3.9057
+{'eval_loss': 0.38556817173957825, 'eval_runtime': 357.3941, 'eval_samples_per_second': 0.839, 'eval_steps_per_second': 0.21, 'eval_rewards/chosen': -2.7128894329071045, 'eval_logps/chosen': -263.7559509277344, 'eval_rewards/rejected': -6.572808265686035, 'eval_logps/rejected': -282.9854431152344, 'eval_kl': 0.0, 'eval_rewards/margins': 3.9057483673095703, 'epoch': 0.99}
 
-## 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 mistralai/Mixtral-8x7B-Instruct-v0.1 with the following prompt:
 
-## Training and evaluation data
+```
+---------------------
+System_prompt:
+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:
+{instructions_formatted}
 
-More information needed
+Na sua memória você tem esse contexto:
+{context}
+
+Lista de requisitos:
+ - Responda de forma natural, mas nunca fale sobre um assunto fora do contexto.
+ - Nunca traga informações do seu próprio conhecimento.
+ - Repito é crucial que você responda usando apenas informações do contexto.
+ - Nunca mencione o contexto fornecido.
+ - Nunca mencione a pergunta fornecida.
+ - Gere a resposta mais útil possível para a pergunta usando informações do conexto acima.
+ - Nunca elabore sobre o porque e como você fez a tarefa, apenas responda.
+
+
+---------------------
+Question:
+{question}
 
-## Training procedure
+
+---------------------
+Response:
+{answer}
+
+
+---------------------
+
+```
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0002
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
+- per_device_train_batch_size: 4
+- per_device_eval_batch_size: 4
 - gradient_accumulation_steps: 4
+- num_gpus: 1
 - total_train_batch_size: 16
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.03
-- training_steps: 145
-- mixed_precision_training: Native AMP
+- optimizer: AdamW
+- lr_scheduler_type: cosine
+- num_steps: 145
+- quantization_type: bitsandbytes
+- LoRA: ("\n  - bits: 4\n  - use_exllama: True\n  - device_map: auto\n  - use_cache: False\n  - lora_r: 16\n  - lora_alpha: 32\n  - lora_dropout: 0.05\n  - bias: none\n  - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n  - task_type: CAUSAL_LM",)
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Kl     | Rewards/margins |
-|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:--------------:|:------:|:---------------:|
-| 0.4403        | 0.34  | 50   | 0.4195          | -1.1683        | -248.3103    | -3.6153          | -253.4100      | 0.5120 | 2.6137          |
-| 0.3518        | 0.68  | 100  | 0.3856          | -2.7129        | -263.7560    | -6.5728          | -282.9854      | 0.0    | 3.9057          |
-
-
 ### Framework versions
 
-- PEFT 0.10.0
-- Transformers 4.39.1
-- Pytorch 2.1.0+cu118
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+- transformers==4.39.1
+- datasets==2.18.0
+- peft==0.10.0
+- safetensors==0.4.2
+- evaluate==0.4.1
+- bitsandbytes==0.43
+- huggingface_hub==0.20.3
+- seqeval==1.2.2
+- optimum==1.17.1
+- auto-gptq==0.7.1
+- gpustat==1.1.1
+- deepspeed==0.14.0
+- wandb==0.16.3
+- # trl==0.8.1
+- git+https://github.com/kawine/trl.git#egg=trl
+- accelerate==0.28.0
+- 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