Hugging Face's logo

RESMP-DEV
/
LLaMutation-Qwen2.5-14B

Text Generation
Transformers
Safetensors
qwen2
conversational
text-generation-inference
Model card Files Community
1
LLaMutation-Qwen2.5-14B / README.md
Kearm's picture
Kearm
Upload folder using huggingface_hub
4f0d83a verified
preview code
|
raw
history blame
4.28 kB
metadata
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-14B
tags:
  - generated_from_trainer
model-index:
  - name: LLaMutation-Qwen2.5-14B-SFFT-v0.0
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: Qwen/Qwen2.5-14B

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

plugins:
  - axolotl.integrations.spectrum.SpectrumPlugin

spectrum_top_fraction: 0.5
# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
spectrum_model_name: Qwen/Qwen2.5-14B

datasets:
  - path: datasets/LLaMutation.jsonl
    type: sharegpt
  - path: datasets/LLaMutationMAX_Train.json
    type: sharegpt

chat_template: chatml
shuffle_merged_datasets: true
val_set_size: 0.1
output_dir: ./LLaMutation-Qwen2.5-14B-SFFT-v0.0

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

# adapter: qlora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# peft_use_dora: true

wandb_project: LLaMutation-Qwen2.5-14B-SFFT-v0.0
wandb_entity:
wandb_watch:
wandb_name: Unit-00
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: linear
learning_rate: 0.0005
max_grad_norm: 3

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 50
evals_per_epoch: 2
saves_per_epoch: 2
save_safetensors: true
hub_model_id: 
hub_strategy: 
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: false  # Changed from true
#   fsdp_use_orig_params: true  # Changed from false
#   fsdp_cpu_ram_efficient_loading: true
#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#   fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
#   fsdp_activation_checkpointing: true
#   fsdp_state_dict_type: SHARDED_STATE_DICT  # Changed from FULL_STATE_DICT
#   fsdp_sharding_strategy: FULL_SHARD
#   fsdp_forward_prefetch: true  # Added
#   fsdp_backward_prefetch: "BACKWARD_POST"  # Added
#   fsdp_backward_prefetch_limit: 1  # Added
#   fsdp_mixed_precision: BF16  # Added

LLaMutation-Qwen2.5-14B-SFFT-v0.0

This model is a fine-tuned version of Qwen/Qwen2.5-14B on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2621

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: 0.0005
  • 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: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
0.3948 0.0237 1 0.3920
0.2392 0.4970 21 0.2500
0.2606 0.9941 42 0.2621

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.3.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.1