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See axolotl config

axolotl version: 0.10.0.dev0

yaml

base_model: Heralax/datagen-pretrain-v1-7b-mistralv0.2
tokenizer_type: AutoTokenizer
model_type: AutoModelForCausalLM
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: 29_mil_asstr.jsonl
    ds_type: json
    type: completion
  - path: 40mil_gutenberg.jsonl
    type: completion
  - path: hle-1_formatted_2mil.jsonl
    type: completion
  - path: 11_mil_fineweb.jsonl
    type: completion
  - path: multiturn_segments_shard_01.json
    type: input_output
  - path: multiturn_segments_shard_02.json
    type: input_output
  - path: singleturn_segments_shard_01.json
    type: input_output
  - path: singleturn_segments_shard_02.json
    type: input_output
  - path: openhermes2_5_shard_01.json
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system
  - path: openhermes2_5_shard_02.json
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system
  - path: openthoughts-1.parquet
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - user
      assistant:
        - assistant
      system:
        - system
  - path: openthoughts-2.parquet
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - user
      assistant:
        - assistant
      system:
        - system
  - path: qwq_10million.jsonl
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system
  - path: bluemoon-6mil.json
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system
dataset_prepared_path: last_run_prepared
output_dir: ./datagen-pretrain-v1-7b-mistralv0.2
seed: 11037
hub_model_id: datagen-sft-1
hub_strategy: every_save

sequence_len: 20000
sample_packing: true
pad_to_sequence_len: false
shuffle_merged_datasets: true

wandb_project: datagen-pretrain-v1-7b-mistralv0.2
wandb_entity:
wandb_watch:
wandb_run_id: 
wandb_log_model:


gradient_accumulation_steps: 50
micro_batch_size: 3
eval_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: constant
learning_rate: 0.000020
weight_decay: 0
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: 
logging_steps: 1
xformers_attention: false # faster
flash_attention: true # slower than xformers

chat_template: chatml

# warmup_ratio: 0.5
# warmup_steps: 0
auto_resume_from_checkpoints: false
warmup_ratio: 0.1
evals_per_epoch: 1
eval_batch_size: 4
val_set_size: 0.01
save_steps: 1000
eval_sample_packing: false
save_total_limit: 2 # NOTE you can afford many more saves with this config due to not storing optimizer states like with normal ones I think.
debug:
special_tokens:
  pad_token: "<unk>"

use_liger_kernel: true


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

datagen-sft-1

This model is a fine-tuned version of Heralax/datagen-pretrain-v1-7b-mistralv0.2 on the 29_mil_asstr.jsonl, the 40mil_gutenberg.jsonl, the hle-1_formatted_2mil.jsonl, the 11_mil_fineweb.jsonl, the multiturn_segments_shard_01.json, the multiturn_segments_shard_02.json, the singleturn_segments_shard_01.json, the singleturn_segments_shard_02.json, the openhermes2_5_shard_01.json, the openhermes2_5_shard_02.json, the openthoughts-1.parquet, the openthoughts-2.parquet, the qwq_10million.jsonl and the bluemoon-6mil.json datasets. It achieves the following results on the evaluation set:

Loss: 0.6304

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
train_batch_size: 3
eval_batch_size: 4
seed: 11037
gradient_accumulation_steps: 50
total_train_batch_size: 150
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 111
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.4533	0.0018	1	2.4612
0.5531	0.9999	558	0.6706
0.5148	1.9981	1116	0.6304

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Heralax/Augmentoolkit-DataSpecialist-v0.1

datagen-sft-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Heralax/
Augmentoolkit-DataSpecialist-v0.1