---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: Qwen/Qwen1.5-0.5B-Chat
model-index:
- name: Qwen1.5-Capybara-0.5B-Chat
  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
base_model: Qwen/Qwen1.5-0.5B-Chat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: markab/Qwen1.5-Capybara-0.5B-Chat
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: every_save
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true # boolean



trust_remote_code:

load_in_8bit: true
load_in_4bit: false
strict: false


datasets:           
  - path: cfahlgren1/Capybara-Converted
    type: sharegpt
    conversation: chatml
    field_system: system      
    field_human: human                                                                         
    field_model: gpt
  - path: markab/coqa_qa_multi
    type: sharegpt                             
    conversation: chatml
    field_system: system
    field_human: human
    field_model: gpt                           
chat_template: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./out

sequence_len: 4000  
sample_packing: false
pad_to_sequence_len: false

#device_map: sequential
#max_memory: {0: "8GB", 1: "8GB", 2: "14GB"}

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: qwen-capybara
wandb_entity:
wandb_watch:
wandb_name: Qwen1.5-Capybara-0.5B-Chat
wandb_log_model: checkpoint

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 0.00022

save_safetensors: true

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 15
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# Qwen1.5-Capybara-0.5B-Chat

This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0419

## 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: 0.00022
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 15
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.164         | 0.0   | 1    | 1.2662          |
| 0.759         | 0.25  | 343  | 1.0705          |
| 0.6798        | 0.5   | 686  | 1.0525          |
| 1.2828        | 0.75  | 1029 | 1.0419          |


### Framework versions

- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0

### Benchmark (MMLU)

```
        Average: 33.35                                                                                                                                                                         
           STEM: 32.20
Social Sciences: 37.00
     Humanities: 31.71
          Other: 33.33
```