File size: 2,014 Bytes
80301f1 09448f7 80301f1 9342283 09448f7 80301f1 39e326e 80301f1 e318091 80301f1 e318091 80301f1 9342283 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
---
license: mit
tags:
- generated_from_trainer
- pytorch
model-index:
- name: dolly-v2-3-openassistant-guanaco
results: []
datasets:
- timdettmers/openassistant-guanaco
library_name: peft
pipeline_tag: text-generation
---
<!-- 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. -->
# dolly-v2-3-openassistant-guanaco
This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on timdettmers/openassistant-guanaco dataset.
## Model description
This is a PEFT model, hence the model file and the config files are
* adapter_model.bin
* adapter_config.bin
This fined-tuned model was created with the following bitsandbytes config<br>
BitsAndBytesConfig(load_in_8bit = True,
bnb_4bit_quant_type = 'nf4',
bnb_4bit_compute_type = torch.bfloat16,
bnb_4bit_use_double_quant = True)
The peft_config is as follows:
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout = 0.1,
r = 64,
bias = "none",
task_type = "CAUSAL_LM",
target_modules = [
'query_key_value',
'dense',
'dense_h_to_4h',
'dense_4h_to_h'
]
)
</br>
## Intended uses & limitations
Model is intended for fair use only.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3 |