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# Llama-2-13b SuperCOT lora checkpoints 

These are my 2nd round of Llama-2-13b SuperCOT Lora checkpoints trained using QLora on the [SuperCOT Dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset) with different parameters closer to the llama 1 supercot. 

### Architecture

- **Model Architecture**: Llama-2-13b
- **Training Algorithm**: QLora

### Training Details

- **Dataset**: [SuperCOT Dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset)
- **Datset type**: alpaca
- **Training Parameters**: [See Here](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/llama-2/qlora.yml)
- **Training Environment**: Axolotl
- **sequence_len**: 4096

### Uploads/merges
Thanks to these gigachads for uploading

- [llama2 13B GGUF by Peepy](https://huggingface.co/Peeepy/SuperCOT-L2-13B-GGUF)
- [llama2 13B GPTQ by Peepy](https://huggingface.co/Peeepy/SuperCOT-L2-13B-GPTQ)


### yml
```
base_model: NousResearch/Llama-2-13b-hf
base_model_config: NousResearch/Llama-2-13b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  
path: kaiokendev/SuperCOT-dataset
  type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora-out/checkpoint-4230

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 16
lora_dropout: 0
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0003

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

warmup_steps: 10
eval_steps: 20
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```


## Acknowledgments

Special thanks to the creators of the datasets in SuperCOT. Additionally, thanks to Kaiokendev for curating the SuperCOT dataset. Thanks to the contributors of the Axolotl.


## Stuff generated from axolotl:

---
library_name: peft
---
## 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.dev0