metadata
base_model: inflatebot/MN-12B-Mag-Mell-R1
library_name: peft
tags:
- axolotl
- generated_from_trainer
model-index:
- name: mn-inf-qlora-mm
results: []
NOT FOR PUBLIC USE
This is only public so we can use it with a merging system that doesn't have access to the org.
See axolotl config
axolotl version: 0.4.1
# Set up for use on 2x24gb cards
# huggingface-cli login --token $hf_key && wandb login $wandb_key
# python -m axolotl.cli.preprocess mn-magmell-patch.yml
# accelerate launch -m axolotl.cli.train mn-magmell-patch.yml
# python -m axolotl.cli.merge_lora mn-magmell-patch.yml
# huggingface-cli upload ToastyPigeon/ms-type1-adventure-s adventure-workspace/merged . --private
base_model: inflatebot/MN-12B-Mag-Mell-R1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
sequence_len: 16384 # 99% vram
min_sample_len: 128
bf16: true
fp16:
tf32: false
flash_attention: true
special_tokens:
# Data
dataset_prepared_path: last_run_prepared
datasets:
- path: botmall/bodinforg-completions
type: completion
warmup_steps: 5
shuffle_merged_datasets: true
save_safetensors: true
special_tokens:
pad_token: "<pad>"
# WandB
wandb_project: Mistral-Nemo-Inflation
wandb_entity:
# Iterations
num_epochs: 1
# Output
output_dir: ./adventure-workspace
hub_model_id: botmall/mn-inf-qlora-mm
hub_strategy: "checkpoint"
# Sampling
sample_packing: true
pad_to_sequence_len: true
# Batching
gradient_accumulation_steps: 1
micro_batch_size: 1
eval_batch_size: 1
gradient_checkpointing: 'unsloth'
gradient_checkpointing_kwargs:
use_reentrant: true
unsloth_cross_entropy_loss: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true
# Evaluation
val_set_size: 20
evals_per_epoch: 5
eval_table_size:
eval_max_new_tokens: 256
eval_sample_packing: false
# LoRA
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.1
lora_target_linear:
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lora_modules_to_save:
# Optimizer
optimizer: paged_adamw_8bit # adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0001
cosine_min_lr_ratio: 0.1
weight_decay: 0.01
max_grad_norm: 10.0
# Misc
train_on_inputs: false
group_by_length: false
early_stopping_patience:
local_rank:
logging_steps: 1
xformers_attention:
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3.json # previously blank
fsdp:
fsdp_config:
# Checkpoints
resume_from_checkpoint:
saves_per_epoch: 1
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
mn-inf-qlora-mm
This model is a fine-tuned version of inflatebot/MN-12B-Mag-Mell-R1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2760
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5697 | 0.0119 | 1 | 2.4926 |
| 2.2991 | 0.2024 | 17 | 2.3356 |
| 2.199 | 0.4048 | 34 | 2.2999 |
| 2.3336 | 0.6071 | 51 | 2.2864 |
| 2.1637 | 0.8095 | 68 | 2.2795 |
| 2.2057 | 1.0119 | 85 | 2.2760 |
Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0