---
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: qlora-yi-34b-200k-rawrr-2
  results: []
---


[<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: ./yi-34b-200k-llamafied
base_model_config: ./yi-34b-200k-llamafied
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: false
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true

bnb_config_kwargs:
    llm_int8_has_fp16_weight: false
    bnb_4bit_quant_type: nf4
    bnb_4bit_use_double_quant: true


bnb_4bit_compute_dtype: torch.bfloat16
torch_dtype: bf16
strict: false

rl: true
datasets:
  - path: /run/media/.../axolotl/datasets/rawrr_v1/
    split: train
    type: apply_chatml
    
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
adapter: qlora
lora_model_dir:
sequence_len: 200
sample_packing: false
lora_r: 4
lora_alpha: 8
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj
lora_fan_in_fan_out:
wandb_project:
wandb_watch:
wandb_run_id:
wandb_log_model:
output_dir: ./qlora-yi-34b-200k-rawrr-2
pad_to_sequence_len: true
micro_batch_size: 1
gradient_accumulation_steps: 16
num_epochs: 1
optimizer: adamw_bnb_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.00003
cosine_min_lr_ratio: 0.2
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
bfloat16: true
flash_optimum: false
gradient_checkpointing: true
early_stopping_patience:
save_safetensors:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
deepspeed: 
seed: 42
warmup_steps: 100
eval_steps: 5000000
save_steps: 80
save_total_limit: 10
eval_table_size: 
eval_table_max_new_tokens:
debug:
weight_decay:
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|startoftext|>"
  eos_token: "<|endoftext|>"
  unk_token: "<unk>"

```

</details><br>

# qlora-yi-34b-200k-rawrr-2

Yi-34B-200K trained via DPO on rawrr_v1 dataset. Sequence length of just 200, that's the max I could fit in with axolotl on RTX 3090 Ti.

## Model description

Looks like DPO worked, even despite being a tiny rank, sequence length and learning rate. \
That's great, I was preparing to start fine-tuning in the cloud but I saw [this](https://github.com/OpenAccess-AI-Collective/axolotl/pull/1060) pull request and I was hopeful it would allow me to squeeze in 34B qlora DPO on 24GB of VRAM. And it did!

## Intended uses & limitations

merge it with yi-34b-200k llama-fied to get a more raw-feeling Yi 34B. In my initial testing on comparison between exl2 4.65bpw base and my DPO fine-tune, my model has no AALM refusals and feels moreso like a true base model. \
Some instruct training still shines through though when you ask it to make an itinerary etc for you.

## 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

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 517

### Training results



### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0