beamaia commited on
Commit
d1f3328
1 Parent(s): ac5ee46

Upload folder using huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +55 -38
README.md CHANGED
@@ -1,68 +1,85 @@
1
  ---
2
  license: mit
3
- library_name: peft
4
  tags:
5
- - trl
6
- - sft
7
- - generated_from_trainer
8
  base_model: HuggingFaceH4/zephyr-7b-beta
9
  model-index:
10
- - name: WeniGPT-QA-Zephyr-7B-3.0.0-SFT
11
  results: []
 
12
  ---
13
 
14
- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
15
- should probably proofread and complete it, then remove this comment. -->
16
 
17
- # WeniGPT-QA-Zephyr-7B-3.0.0-SFT
18
 
19
- This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
- - Loss: 1.2232
22
 
23
- ## Model description
24
 
25
- More information needed
26
 
27
- ## Intended uses & limitations
28
 
29
- More information needed
30
 
31
- ## Training and evaluation data
 
 
 
 
32
 
33
- More information needed
 
 
 
 
34
 
35
- ## Training procedure
 
 
 
36
 
37
  ### Training hyperparameters
38
 
39
  The following hyperparameters were used during training:
40
  - learning_rate: 0.0002
41
- - train_batch_size: 2
42
- - eval_batch_size: 2
43
- - seed: 42
44
- - distributed_type: multi-GPU
45
- - num_devices: 4
46
  - gradient_accumulation_steps: 8
 
47
  - total_train_batch_size: 64
48
- - total_eval_batch_size: 8
49
- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
- - lr_scheduler_type: linear
51
- - lr_scheduler_warmup_ratio: 0.03
52
- - training_steps: 96
53
- - mixed_precision_training: Native AMP
54
 
55
  ### Training results
56
 
57
- | Training Loss | Epoch | Step | Validation Loss |
58
- |:-------------:|:-----:|:----:|:---------------:|
59
- | 1.2815 | 1.52 | 50 | 1.2712 |
60
-
61
-
62
  ### Framework versions
63
 
64
- - PEFT 0.10.0
65
- - Transformers 4.39.1
66
- - Pytorch 2.1.0+cu118
67
- - Datasets 2.18.0
68
- - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: mit
3
+ library_name: "trl"
4
  tags:
5
+ - SFT
6
+ - WeniGPT
 
7
  base_model: HuggingFaceH4/zephyr-7b-beta
8
  model-index:
9
+ - name: Weni/WeniGPT-QA-Zephyr-7B-3.0.0-SFT
10
  results: []
11
+ language: ['pt']
12
  ---
13
 
14
+ # Weni/WeniGPT-QA-Zephyr-7B-3.0.0-SFT
 
15
 
16
+ This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta] on the dataset Weni/WeniGPT-QA-Binarized-1.2.0 with the SFT trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
17
 
 
18
  It achieves the following results on the evaluation set:
19
+ {'eval_loss': 1.2232297658920288, 'eval_runtime': 84.7619, 'eval_samples_per_second': 2.761, 'eval_steps_per_second': 0.354, 'epoch': 2.92}
20
 
21
+ ## Intended uses & limitations
22
 
23
+ This model has not been trained to avoid specific intructions.
24
 
25
+ ## Training procedure
26
 
27
+ Finetuning was done on the model HuggingFaceH4/zephyr-7b-beta with the following prompt:
28
 
29
+ ```
30
+ ---------------------
31
+ Portuguese:
32
+ <|system|>
33
+ 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.
34
 
35
+ Contexto: {context}</s>
36
+ <|user|>
37
+ {question}</s>
38
+ <|assistant|>
39
+ {chosen_response}</s>
40
 
41
+
42
+ ---------------------
43
+
44
+ ```
45
 
46
  ### Training hyperparameters
47
 
48
  The following hyperparameters were used during training:
49
  - learning_rate: 0.0002
50
+ - per_device_train_batch_size: 2
51
+ - per_device_eval_batch_size: 2
 
 
 
52
  - gradient_accumulation_steps: 8
53
+ - num_gpus: 4
54
  - total_train_batch_size: 64
55
+ - optimizer: AdamW
56
+ - lr_scheduler_type: constant_with_warmup
57
+ - num_steps: 96
58
+ - quantization_type: bitsandbytes
59
+ - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 32\n - lora_alpha: 64\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
 
60
 
61
  ### Training results
62
 
 
 
 
 
 
63
  ### Framework versions
64
 
65
+ - transformers==4.39.1
66
+ - datasets==2.18.0
67
+ - peft==0.10.0
68
+ - safetensors==0.4.2
69
+ - evaluate==0.4.1
70
+ - bitsandbytes==0.43
71
+ - huggingface_hub==0.20.3
72
+ - seqeval==1.2.2
73
+ - optimum==1.17.1
74
+ - auto-gptq==0.7.1
75
+ - gpustat==1.1.1
76
+ - deepspeed==0.14.0
77
+ - wandb==0.16.3
78
+ - trl==0.8.1
79
+ - accelerate==0.28.0
80
+ - coloredlogs==15.0.1
81
+ - traitlets==5.14.1
82
+ - autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
83
+
84
+ ### Hardware
85
+ - Cloud provided: runpod.io