12b-mn-dans-reasoning-test-4

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

axolotl version: 0.8.0.dev0

base_model: Dans-DiscountModels/Mistral-Nemo-Base-2407-ChatML-Mod
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code:

# wandb configuration
wandb_project: 12b-mn-dans-reasoning-test
wandb_watch:

wandb_run_id: V0.0.3-1-3 # V{Version}-{Run Number}-{Attempt Number}
wandb_log_model:

# push checkpoints to hub
hub_model_id: Dans-DiscountModels/12b-mn-dans-reasoning-test-4
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: "every_save"
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true

# where to save the finished model to
output_dir: ./12b-mn-dans-reasoning-test

save_safetensors: true

# dataset settings (local or huggingface repo)
datasets:
  - path: PocketDoc/Dans-Reasoningmaxx-NaturalReasoning
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Reasoningmaxx-WebInstruct
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Benchmaxx-COT
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Logicmaxx-SAT-AP
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Assistantmaxx-Opus-Merge
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Assistantmaxx-sonnetorca-subset
    type: dan-chat-advanced

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

adapter:
lora_model_dir:

lora_r: 128
lora_alpha: 128
lora_dropout: 0.1
lora_target_linear: True
lora_target_modules:
lora_modules_to_save:
  - embed_tokens
  - lm_head
lora_fan_in_fan_out:
peft_use_rslora: true

dataset_prepared_path: ./12b-mn-dans-reasoning-test-data
val_set_size: 0.005

sequence_len: 8192

sample_packing: true
eval_sample_packing: true

pad_to_sequence_len: true

gradient_checkpointing: true
# gradient_checkpointing_kwargs:
# use_reentrant: false

gradient_accumulation_steps: 1
micro_batch_size: 4

num_epochs: 2

optimizer: came_pytorch

lr_scheduler: rex
learning_rate: 0.0000015
cosine_min_lr_ratio: 0.1

weight_decay: 0.1

max_grad_norm: 0.1

train_on_inputs: false
group_by_length: true

bf16: true
fp16: false
tf32: false

early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true

local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.05

evals_per_epoch: 16
eval_table_size:
eval_max_new_tokens:

saves_per_epoch: 8
save_total_limit: 1

debug: false

deepspeed: deepspeed_configs/zero3_bf16.json

fsdp:
fsdp_config:

special_tokens:

</details><br>

12b-mn-dans-reasoning-test-4

This model is a fine-tuned version of Dans-DiscountModels/Mistral-Nemo-Base-2407-ChatML-Mod on the PocketDoc/Dans-Reasoningmaxx-NaturalReasoning, the PocketDoc/Dans-Reasoningmaxx-WebInstruct, the PocketDoc/Dans-Benchmaxx-COT, the PocketDoc/Dans-Logicmaxx-SAT-AP, the PocketDoc/Dans-Assistantmaxx-Opus-Merge and the PocketDoc/Dans-Assistantmaxx-sonnetorca-subset datasets. It achieves the following results on the evaluation set:

• Loss: 0.6204

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: 1.5e-06
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 16
• total_eval_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 38
• num_epochs: 2.0

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.4.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.1