---
library_name: peft
license: llama2
base_model: lmsys/vicuna-13b-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: df36ec19-0bef-467d-9b7e-8a3106fba5e8
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: qlora
base_model: lmsys/vicuna-13b-v1.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 918636bf96f6fa65_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/918636bf96f6fa65_train_data.json
  type:
    field_input: description
    field_instruction: input persona
    field_output: synthesized text
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/df36ec19-0bef-467d-9b7e-8a3106fba5e8
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 1
mlflow_experiment_name: /tmp/918636bf96f6fa65_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.002
wandb_entity: null
wandb_mode: online
wandb_name: 9a466c78-74d9-4760-a15c-e4cf2942511b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9a466c78-74d9-4760-a15c-e4cf2942511b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# df36ec19-0bef-467d-9b7e-8a3106fba5e8

This model is a fine-tuned version of [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8728

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1308        | 0.0002 | 1    | 1.1573          |
| 0.9154        | 0.0040 | 25   | 0.9314          |
| 0.744         | 0.0080 | 50   | 0.8872          |
| 0.8522        | 0.0121 | 75   | 0.8755          |
| 0.8483        | 0.0161 | 100  | 0.8728          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1