---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: rishiraj/CatPPT-base
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. -->

[<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: `0.3.0`
```yaml
base_model: rishiraj/CatPPT-base
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

model_config:
  output_router_logits: true

datasets:
  - path: teknium/openhermes
    type: alpaca
    prompt_style: chatml
  - path: garage-bAInd/Open-Platypus
    type: alpaca
    prompt_style: chatml
  - path: LDJnr/Capybara
    type: sharegpt
    conversation: chatml
  - path: datasets/samantha-1.1.json
    type: sharegpt
    conversation: chatml
  - path: datasets/grammarly-coedit-alpaca.jsonl
    type: alpaca
    prompt_style: chatml
  - path: datasets/dolphin-coder-codegen.jsonl
    type: alpaca_w_system.load_open_orca_chatml
  - path: datasets/dolphin-coder-translate.jsonl
    type: alpaca_w_system.load_open_orca_chatml
  - path: datasets/data-evol_instruct-decontaminated-alpaca.jsonl
    type: alpaca
    prompt_style: chatml
  - path: datasets/data-oss_instruct-decontaminated-alpaca.jsonl   
    type: alpaca
    prompt_style: chatml
  - path: jondurbin/airoboros-3.2
    type: sharegpt
    conversations: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora-out-2
seed: 420

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 3
num_epochs: 1.5
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 0
evals_per_epoch: 20
eval_table_size:
saves_per_epoch: 20
debug:
deepspeed:
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"
trust_remote_code: true

```

</details><br>

# qlora-out-2

This model is a fine-tuned version of [rishiraj/CatPPT-base](https://huggingface.co/rishiraj/CatPPT-base) on multiple datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4258

## 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: 3
- eval_batch_size: 3
- seed: 420
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 18
- total_eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7691        | 0.0   | 1    | 0.7027          |
| 0.5556        | 0.05  | 135  | 0.5808          |
| 0.597         | 0.1   | 270  | 0.5488          |
| 0.5979        | 0.15  | 405  | 0.5277          |
| 0.4693        | 0.2   | 540  | 0.5108          |
| 0.5123        | 0.25  | 675  | 0.4978          |
| 0.4394        | 0.3   | 810  | 0.4883          |
| 0.46          | 0.35  | 945  | 0.4802          |
| 0.4472        | 0.4   | 1080 | 0.4748          |
| 0.47          | 0.45  | 1215 | 0.4687          |
| 0.4249        | 0.5   | 1350 | 0.4637          |
| 0.4823        | 0.55  | 1485 | 0.4599          |
| 0.4209        | 0.6   | 1620 | 0.4555          |
| 0.4909        | 0.65  | 1755 | 0.4517          |
| 0.4663        | 0.7   | 1890 | 0.4470          |
| 0.4215        | 0.75  | 2025 | 0.4437          |
| 0.4267        | 0.8   | 2160 | 0.4398          |
| 0.4109        | 0.85  | 2295 | 0.4364          |
| 0.4099        | 0.9   | 2430 | 0.4331          |
| 0.447         | 0.95  | 2565 | 0.4298          |
| 0.4412        | 1.0   | 2700 | 0.4272          |
| 0.3838        | 1.03  | 2835 | 0.4287          |
| 0.4262        | 1.08  | 2970 | 0.4274          |
| 0.3889        | 1.13  | 3105 | 0.4263          |
| 0.3114        | 1.18  | 3240 | 0.4255          |
| 0.3685        | 1.23  | 3375 | 0.4256          |
| 0.392         | 1.28  | 3510 | 0.4253          |
| 0.3751        | 1.33  | 3645 | 0.4255          |
| 0.3756        | 1.39  | 3780 | 0.4256          |
| 0.3108        | 1.44  | 3915 | 0.4258          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.0