peft safetensors llama axolotl generated_from_trainer base_model:152334H/miqu-1-70b-sf license:mit 4-bit bitsandbytes region:us
---
license: mit
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: 152334H/miqu-1-70b-sf
model-index:
- name: miqu-limarp-70b
  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: `0.3.0`
```yaml
base_model: 152334H/miqu-1-70b-sf
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: NobodyExistsOnTheInternet/LimaRP
    type: sharegpt
    conversation: chatml
  - path: Doctor-Shotgun/no-robots-sharegpt
    type: sharegpt
    conversation: chatml

chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./miqu-lora
save_safetensors: true

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true

lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: miqu-lora
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: paged_lion_8bit
lr_scheduler: cosine
learning_rate: 0.00025

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
save_total_limit: 2

warmup_steps: 10
eval_table_size:
weight_decay: 0
special_tokens:
  bos_token: "<s>"
  eos_token: "<|im_end|>"
  unk_token: "</s>"


tokens:
    - "<|im_start|>"
    - "<|im_end|>"

neftune_noise_alpha: 5


hub_model_id: NobodyExistsOnTheInternet/miqu-limarp-70b
hub_strategy: all_checkpoints
hf_use_auth_token: true

```

</details><br>

# miqu-limarp-70b

This model is a fine-tuned version of [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) on the None dataset.

## 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: 0.00025
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 10
- num_epochs: 4

### Training results



### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- 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
