---
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