cgifbribcgfbi/gemma-2-27b-chem-3-5-sonnet-replicate-old
07
<!-- 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.11.0
base_model: byroneverson/gemma-2-27b-it-abliterated
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: gemma-2-27b-chem-3-5-sonnet-replicate-old
output_dir: ./outputs/out/gemma-2-27b-chem-3-5-sonnet-replicate-old
hub_model_id: cgifbribcgfbi/gemma-2-27b-chem-3-5-sonnet-replicate-old
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: 3-5-sonnet-replicate-old_user_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
# val_set_size: 0.05
# eval_sample_packing: False
save_safetensors: true
sequence_len: 2655
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
lora_target_linear: false
lora_modules_to_save:
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Gemma2DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
</details><br>
gemma-2-27b-chem-3-5-sonnet-replicate-old
This model is a fine-tuned version of byroneverson/gemma-2-27b-it-abliterated on the 3-5-sonnet-replicate-olduser5000.jsonl 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: 2e-05
- trainbatchsize: 4
- evalbatchsize: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- totaltrainbatch_size: 32
- totalevalbatch_size: 32
- optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 10
- training_steps: 440
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
