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--- |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: intervitens/internlm2-base-20b-llama |
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model-index: |
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- name: internlm-limarp-lora |
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results: [] |
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--- |
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Don't use this yet, there's a problem with the llamafied internlm2 tokenizer. |
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Prompt format: ChatML. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.3.0` |
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```yaml |
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base_model: /data/internlm2-base-20b-llama |
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model_type: LlamaForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_llama_derived_model: true |
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load_in_8bit: true |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: /data/train-all-8k.jsonl |
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type: completion |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: /data/internlm-limarp-lora-out |
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sequence_len: 8192 |
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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: 64 |
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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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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 4 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.00002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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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: 10 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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</details><br> |
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# internlm-limarp-lora |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1216 |
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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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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: True |
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- load_in_4bit: False |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.3563 | 0.01 | 1 | 2.3995 | |
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| 2.1815 | 0.25 | 37 | 2.2693 | |
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| 2.1364 | 0.51 | 74 | 2.1684 | |
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| 2.1355 | 0.76 | 111 | 2.1526 | |
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| 2.1624 | 1.03 | 148 | 2.1435 | |
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| 2.1326 | 1.28 | 185 | 2.1367 | |
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| 1.9987 | 1.54 | 222 | 2.1330 | |
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| 2.0494 | 1.79 | 259 | 2.1291 | |
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| 2.0505 | 2.04 | 296 | 2.1266 | |
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| 2.075 | 2.3 | 333 | 2.1243 | |
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| 2.0183 | 2.55 | 370 | 2.1229 | |
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| 2.1047 | 2.81 | 407 | 2.1227 | |
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| 2.1309 | 3.06 | 444 | 2.1218 | |
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| 2.1249 | 3.31 | 481 | 2.1214 | |
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| 2.1423 | 3.57 | 518 | 2.1214 | |
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| 2.0913 | 3.82 | 555 | 2.1216 | |
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### Framework versions |
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- PEFT 0.7.0 |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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