See axolotl config
axolotl version: 0.8.0
base_model: AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
unfrozen_parameters:
- lm_head.weight
- model.embed_tokens.weight
datasets:
- path: AlexHung29629/train_0415_input_output
type: input_output
- path: AlexHung29629/glaive-function-calling-v2-mistral
type:
system_prompt: ""
field_system: system
field_instruction: input
field_output: output
format: "{instruction}"
no_input_format: "{instruction}"
dataset_prepared_path: ./sft_dataprep/
val_set_size: 0
output_dir: ./placeholder_embed/
shuffle_merged_datasets: false
sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
hub_model_id: AlexHung29629/model_step1
wandb_project: TP1_2025_05
wandb_entity:
wandb_watch:
wandb_name: Mistral-24B-SFT-250522_embed
wandb_log_model: checkpoint
use_tensorboard: true
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
max_steps: 1000
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 2e-5
max_grad_norm: 1.0
bf16: true
tf32: false
#gradient_checkpointing: false
#gradient_checkpointing_kwargs:
# use_reentrant: false
logging_steps: 1
flash_attention: true
xformers_attention: false
sdp_attention: false
warmup_ratio: 0.01
saves_per_epoch:
save_steps: 1000
weight_decay: 0
#deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: true
fsdp_use_orig_params: true
fsdp_cpu_ram_efficient_loading: true
fsdp_activation_checkpointing: true
fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
special_tokens:
pad_token: "<pad>"
added_tokens_overrides: # Dict[int, str]
20: "<think>"
21: "</think>"
seed: 42
model_step1
This model is a fine-tuned version of AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text on an unknown dataset.
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH 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
- training_steps: 1000
Framework versions
- Transformers 4.51.0
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
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