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--- |
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library_name: peft |
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license: mit |
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base_model: THUDM/GLM-4-32B-0414 |
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tags: |
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- axolotl |
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- generated_from_trainer |
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datasets: |
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- anthracite-core/magnum-v5-sft-proto-glm4-instruct-rev1 |
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model-index: |
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- name: magnum-v5-sft-prototype-glm4-32b-lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.8.0` |
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```yaml |
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base_model: THUDM/GLM-4-32B-0414 |
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#base_model_ignore_patterns: "*/*" |
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# optionally might have model_type or tokenizer_type |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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# Automatically upload checkpoint and final model to HF |
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hub_model_id: anthracite-core/magnum-v5-sft-prototype-glm4-32b-lora |
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hub_strategy: "all_checkpoints" |
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push_dataset_to_hub: |
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hf_use_auth_token: true |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: anthracite-core/magnum-v5-sft-proto-glm4-instruct-rev1 |
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ds_type: parquet |
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type: |
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shuffle_merged_datasets: true |
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dataset_prepared_path: ./data/magnum-32b-data |
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val_set_size: 0.01 |
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output_dir: ./data/32b-lora-out |
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plugins: |
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- axolotl.integrations.liger.LigerPlugin |
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
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#liger_rope: false |
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liger_rms_norm: true |
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liger_layer_norm: true |
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liger_glu_activation: true |
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liger_fused_linear_cross_entropy: true |
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cut_cross_entropy: true |
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sequence_len: 32768 |
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sample_packing: true |
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eval_sample_packing: true |
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pad_to_sequence_len: true |
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adapter: lora |
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lora_model_dir: |
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lora_r: 128 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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peft_use_rslora: true |
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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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wandb_project: 32b-magnum-lora |
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wandb_entity: |
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wandb_watch: |
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wandb_name: run4-Lora-0.001-clip |
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wandb_log_model: |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 1 |
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num_epochs: 2 |
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optimizer: paged_ademamix_8bit |
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lr_scheduler: cosine |
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learning_rate: 2e-4 |
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max_grad_norm: 0.001 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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s2_attention: |
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warmup_steps: 40 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: |
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saves_per_epoch: 2 |
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debug: |
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deepspeed: ./deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.01 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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``` |
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</details><br> |
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# magnum-v5-sft-prototype-glm4-32b-lora |
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This model is a fine-tuned version of [THUDM/GLM-4-32B-0414](https://huggingface.co/THUDM/GLM-4-32B-0414) on the anthracite-core/magnum-v5-sft-proto-glm4-instruct-rev1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1075 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 8 |
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- optimizer: Use paged_ademamix_8bit and the args are: |
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No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 40 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.3541 | 0.0024 | 1 | 1.3336 | |
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| 1.1718 | 0.2503 | 103 | 1.1633 | |
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| 1.1976 | 0.5006 | 206 | 1.1460 | |
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| 1.095 | 0.7509 | 309 | 1.1339 | |
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| 1.1076 | 1.0 | 412 | 1.1213 | |
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| 1.1063 | 1.2503 | 515 | 1.1128 | |
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| 1.1214 | 1.5006 | 618 | 1.1089 | |
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| 1.0286 | 1.7509 | 721 | 1.1075 | |
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### Framework versions |
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- PEFT 0.15.1 |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
