Pretraining-SCPWiki-032025-12B-Instruct

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

axolotl version: 0.8.0.dev0

base_model: mistralai/Mistral-Nemo-Base-2407
# optionally might have model_type or tokenizer_type
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/Pretraining-SCPWiki-032025-12B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AiAF/Pretraining-SCPWiki-032025-7B-Instruct-pretraining.jsonl
    type: completion
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/out/Pretraining-SCPWiki-032025-12B-V1

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

wandb_project: "LLM-Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "Pretraining-SCPWiki-032025-12B-V1"
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

save_total_limit: 30
warmup_steps: 10
evals_per_epoch: 20
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 20
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<pad>"
  bos_token: "<s>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"

</details><br>

Pretraining-SCPWiki-032025-12B-Instruct

This model is a fine-tuned version of mistralai/Mistral-Nemo-Base-2407 on the AiAF/Pretraining-SCPWiki-032025-7B-Instruct-pretraining.jsonl dataset. It achieves the following results on the evaluation set:

• Loss: 1.5467

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: 10
• num_epochs: 1.0

Training results

Framework versions

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