gemma-2-logql

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

base_model: google/gemma-2-9b

sequence_len: 1024

# base model weight quantization
load_in_8bit: true
# load_in_4bit: true

# attention implementation
flash_attention: true

# finetuned adapter config
adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
  - embed_tokens
  - lm_head

# if training fails, uncomment above
# for details, see https://github.com/huggingface/peft/issues/334#issuecomment-1561727994

###
# Dataset Configuration: sqlqa
###
# datasets:
#   - path: data.jsonl
#     type: alpaca

datasets:
  - path: public_train_data.jsonl
    ds_type: json
    type:
      field_instruction: instruction
      field_input: input
      field_output: output
      format: |-
        [INST] {instruction}
        {input} [/INST]

chat_template: gemma
tokens:
  - "[INST]"
  - " [/INST]"
  - "[QL]"
  - " [/QL]"
  - "[EXPLANATION]"
  - " [/EXPLANATION]"
# dataset formatting config

special_tokens:
  pad_token: <|end_of_text|>

val_set_size: 0.05

###
# Training Configuration
###

# masks the input messages so that the model learns and understands the language w/o being reliant on the input
train_on_inputs: false
# random seed for better reproducibility
seed: 117

# optimizer config
optimizer: adamw_bnb_8bit
learning_rate: 0.0001
lr_scheduler: cosine
num_epochs: 4
micro_batch_size: 4
gradient_accumulation_steps: 1
warmup_steps: 10

# axolotl saving config
dataset_prepared_path: last_run_prepared
output_dir: ./lora-out

# logging and eval config
logging_steps: 1
eval_steps: 0.05

# training performance optimization config
bf16: auto
tf32: false
gradient_checkpointing: true

###
# Miscellaneous Configuration
###

# when true, prevents over-writing the config from the CLI
strict: false

# "Don't mess with this, it's here for accelerate and torchrun" -- axolotl docs
local_rank:

# WANDB
wandb_mode:
wandb_project:
wandb_watch:
wandb_name:
wandb_run_id:

# Multi-GPU
# deepspeed: /root/axolotl/deepspeed_configs/zero3_bf16.json
# deepspeed: zero3_bf16.json
# deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
deepspeed:
fsdp:
fsdp_config:

</details><br>

lora-out

This model is a fine-tuned version of google/gemma-2-9b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0077

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: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 117
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 16
• total_eval_batch_size: 16
• 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

• PEFT 0.13.0
• Transformers 4.45.1
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.20.0