qwen3-32b-tos-game-docs-c4-lora-round2

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

axolotl version: 0.10.0

base_model: Qwen/Qwen3-32B
# Older Axolotl explicit-class loading skips its ZeRO-3 initialization helper.
# AutoModel resolves to Qwen3 while taking the sharded-loading branch.
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false

# Document + C4 CPT, approximately 1:1 raw training tokens.
# Reuse the same published datasets and splits as the 8B C4 run.
# Keep these fixed across model sizes. Three epochs preserve three passes over the game training
# documents, approximately doubling total tokens versus the document-only run.
datasets:
  - path: cfierro/tos_game_synthetic_docs
    type: completion
    split: train_verified_round2[:95%]
    field: text
  - path: cfierro/c4-en-tos-game-replay
    type: completion
    split: round2_10k_docs
    field: text

# Raw document completion: no chat template or thinking traces are inserted.
# Long documents are chunked during completion preprocessing.
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
val_set_size: 0
# Evaluate only the same round2 held-out game documents as the 8B/14B configs.
test_datasets:
  - path: cfierro/tos_game_synthetic_docs
    type: completion
    split: train_verified_round2[95%:]
    field: text
dataset_prepared_path: /scratch/project/eu-26-55/knowledge-ft/axolotl/datasets/qwen3-32b/tos-game-docs-c4-lora-round2
output_dir: /scratch/project/eu-26-55/knowledge-ft/axolotl/models/qwen3-32b-tos-game-docs-c4-lora-round2

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

# Four 48GB GPUs with ZeRO-3; the two-GPU trial loaded but OOMed in evaluation.
# See ../README.md for launch commands. BF16 LoRA with activation checkpointing.
# Effective batch: 8 packed sequences, at most 32,768 tokens per update.
micro_batch_size: 1
gradient_accumulation_steps: 2
num_epochs: 3

optimizer: adamw_bnb_8bit
learning_rate: 3.5e-5
lr_scheduler: cosine
warmup_ratio: 0.03
weight_decay: 0.0

bf16: true
tf32: false
gradient_checkpointing: true
flash_attention: true

# Shard the frozen base weights as well as trainable adapter state.
deepspeed: deepspeed_configs/zero3.json

logging_steps: 1
eval_strategy: epoch
save_strategy: epoch
save_total_limit: 3
load_best_model_at_end: true
metric_for_best_model: eval_loss
greater_is_better: false
resume_from_checkpoint:

wandb_project: synthetic-document-ft
wandb_entity: cfierro
wandb_watch:
wandb_name: qwen3-32b-tos-game-docs-c4-lora-round2
wandb_log_model: "false"

hub_model_id: cfierro/qwen3-32b-tos-game-docs-c4-lora-round2
hub_strategy: end
hub_private_repo: false

# IMPORTANT for evaluation: Qwen3-32B defaults to thinking. Pass
# enable_thinking=False to tokenizer.apply_chat_template(...), both before
# and after training. This is an inference setting; raw completion training
# does not invoke the chat template and cannot set that inference default.
# Preserve the model tokenizer's EOS and padding tokens.
#
# CPU preprocessing on Hendrix (keep BF16 enabled for GPU training):
#   CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess axolotl_configs/continued_pretraining/round2/qwen3-32b-tos-game-docs-c4-lora-round2.yaml --bf16=False --debug

</details><br>

qwen3-32b-tos-game-docs-c4-lora-round2

This model is a fine-tuned version of Qwen/Qwen3-32B on the cfierro/tos_game_synthetic_docs and the cfierro/c4-en-tos-game-replay datasets. It achieves the following results on the evaluation set:

• Loss: 1.1895

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: 3.5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• total_eval_batch_size: 4
• 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: 64
• training_steps: 2148

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.57.3
• Pytorch 2.9.0+cu128
• Datasets 3.5.0
• Tokenizers 0.22.2