See axolotl config

axolotl version: 0.8.0.dev0

base_model: mistralai/Mistral-7B-v0.1
# optionally might have model_type or tokenizer_type
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/Pretrained-Codename-75567-V1

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AiAF/Codename-75567-Pretrainin.jsonl
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/out

sequence_len: 512
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: "LLM-Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "Codename-75567-V1"
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

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

warmup_steps: 1
evals_per_epoch: 5
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

Pretrained-Codename-75567-V1

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the AiAF/Codename-75567-Pretrainin.jsonl dataset. It achieves the following results on the evaluation set:

• Loss: 1.5809

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: 5e-06
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 2
• num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.7902	0.3333	1	1.7724
1.8972	0.6667	2	1.6288
1.6898	1.0	3	1.5141
1.3171	1.3333	4	1.5028
1.1106	1.6667	5	1.5158
1.149	2.0	6	1.5504
0.8633	2.3333	7	1.5803
0.767	2.6667	8	1.5793
0.7649	3.0	9	1.5767
0.6748	3.3333	10	1.5816
0.6898	3.6667	11	1.5816
0.6616	4.0	12	1.5809

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0