# === Model Configuration ===
base_model: THUDM/GLM-4-32B-0414  # e.g. "mistralai/Mistral-Small-24B-Instruct-2501"
load_in_8bit: false
load_in_4bit: true

# === Training Setup ===
num_epochs: 2
micro_batch_size: 3
gradient_accumulation_steps: 2
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

# === Hyperparameter Configuration ===
optimizer: adamw_8bit
# Apollo-mini configuration:
#optim_args: "proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200"
# Regular Apollo configuration:
# optim_args: 
#optim_target_modules: all_linear
learning_rate: 1e-5
lr_scheduler: rex
weight_decay: 0.01
warmup_ratio: 0.05

# === LoRA Configuration ===
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.25
lora_target_modules:
lora_target_linear: true

# === Data Configuration ===
datasets:
  - path: allura-org/inkmix-v3.0
    type: chat_template
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: all

dataset_prepared_path: last_run_prepared
chat_template: jinja
chat_template_jinja: |
  [gMASK]<sop>{%- for msg in messages %}{%- if msg.role == 'system' %}<|system|>
  {{ msg.content }}{%- elif msg.role == 'user' %}<|user|>
  {{ msg.content }}{%- elif msg.role == 'assistant' %}<|assistant|>
  {{ msg.content }}{%- endif %}{%- endfor %}{% if add_generation_prompt %}<|assistant|>{% else %}<|user|>{% endif %}

# === Plugins ===
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

# === Hardware Optimization ===
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
cut_cross_entropy: true
deepspeed: deepspeed_configs/zero3_bf16.json

# === Wandb Tracking ===
wandb_project: glm4-32b-inkmix-v3

# === Checkpointing ===
saves_per_epoch: 2
save_total_limit: 3

# === Advanced Settings ===
output_dir: /workspace/ckpts
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
logging_steps: 1
trust_remote_code: true

workspace/ckpts

This model is a fine-tuned version of THUDM/GLM-4-32B-0414 on the allura-org/inkmix-v3.0 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: 1e-05
train_batch_size: 3
eval_batch_size: 3
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 48
total_eval_batch_size: 24
optimizer: Use adamw_8bit 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: 31
num_epochs: 2.0

Training results

Framework versions

PEFT 0.15.1
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

allura-forge/remnant-glm4-32b-adpt

workspace/ckpts

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

allura-forge/
remnant-glm4-32b-adpt