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pbe-lora

Text Generation
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llama
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pbe-lora
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---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
model-index:
- name: lora-logo_fix_full_deepseek7b_ds33i_lr_0.0002_alpha_512_r_512
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
adapter: lora
base_model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
bf16: auto
dataset_prepared_path: ./logo_ds_preprocess_list_gpt35
datasets:
- path: ../logo/fix_deepseek_synthetic_training_data_full.jsonl
  type:
    field_instruction: input
    field_output: output
    format: '### Instruction:

      {input}

      ### Response:

      '
    no_input_format: '{instruction}'
debug: null
deepspeed: ./deepspeed_configs/zero2.json
early_stopping_patience: null
eval_sample_packing: true
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
is_llama_derived_model: true
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 512
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 512
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 8
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: ./lora-logo_fix_full_deepseek7b_ds33i_lr_0.0002_alpha_512_r_512
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 1800
special_tokens:
  bos_token: "<\uFF5Cbegin\u2581of\u2581sentence\uFF5C>"
  eos_token: <|EOT|>
strict: true
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: logo_fix_full_deepseek7b_ds33i_lr_0.0002_alpha_512_r_512
wandb_project: pbe-axo
wandb_watch: null
warmup_steps: 20
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# lora-logo_fix_full_deepseek7b_ds33i_lr_0.0002_alpha_512_r_512

This model is a fine-tuned version of [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4023

## Model description

More information needed

## Intended uses & limitations

More information needed

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3105        | 0.0   | 1    | 2.3643          |
| 0.463         | 0.25  | 78   | 0.4702          |
| 0.4169        | 0.5   | 156  | 0.4442          |
| 0.4411        | 0.75  | 234  | 0.4278          |
| 0.4635        | 1.0   | 312  | 0.4184          |
| 0.3626        | 1.23  | 390  | 0.4134          |
| 0.3622        | 1.48  | 468  | 0.4074          |
| 0.3903        | 1.73  | 546  | 0.4003          |
| 0.3737        | 1.98  | 624  | 0.3954          |
| 0.3169        | 2.21  | 702  | 0.4044          |
| 0.338         | 2.46  | 780  | 0.4030          |
| 0.3237        | 2.71  | 858  | 0.4022          |
| 0.2976        | 2.96  | 936  | 0.4023          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0