---
license: mit
base_model: 152334H/miqu-1-70b-sf
tags:
- generated_from_trainer
model-index:
- name: qlora-out
results: []
---
[](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```yaml
base_model: 152334H/miqu-1-70b-sf
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Drewskidang/rag
type: alpaca
prompt_style: chatml
- path: Drewskidang/output
type: alpaca
prompt_style: chatml
- path: Drewskidang/Instruct
type: alpaca
prompt_style: chatml
- path: Drewskidang/Preference
type: alpaca
prompt_style: chatml
- path: lighteval/legal_summarization
name: BillSum
type: summarizetldr
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./qlora-out
## You can optionally freeze the entire model and unfreeze a subset of parameters
unfrozen_parameters:
- lm_head.*
#- model.embed_tokens.*
#- model.layers.2[0-9]+.block_sparse_moe.gate.*
# - model.layers.2[0-9]+.block_sparse_moe.experts.*
# - model.layers.3[0-9]+.block_sparse_moe.gate.*
# - model.layers.3[0-9]+.block_sparse_moe.experts.*
model_config:
output_router_logits: true
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
wandb_project: Leak_Mistral
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 10
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
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: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
tokens:
- "<|im_start|>"
trust_remote_code: true
```
# qlora-out
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: 0.0002
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 160
- total_eval_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0