211 lines
5.3 KiB
Markdown
211 lines
5.3 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- generated_from_trainer
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- axolotl
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language:
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- it
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- en
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pipeline_tag: text-generation
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datasets:
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- ReDiX/everyday-conversations-ita
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- ReDiX/dataforge-cleaned
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---
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# Qwen2.5-0.5B-Instruct-ITA
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This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [ReDiX/DataForge](https://huggingface.co/datasets/ReDiX/DataForge) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4100
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## Model description
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This model is an example of finetuning a sLLM. Italian eval improved and the model learned as espected from the training data
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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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| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
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|------------|------:|------|-----:|--------|---|-----:|---|-----:|
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|arc_it | 2|none | 0|acc |↑ |0.2378|± |0.0125|
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| | |none | 0|acc_norm|↑ |0.2823|± |0.0132|
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|hellaswag_it| 1|none | 0|acc |↑ |0.3163|± |0.0049|
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| | |none | 0|acc_norm|↑ |0.3800|± |0.0051|
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|m_mmlu_it | 0|none | 5|acc |↑ |0.381 |± |0.0042|
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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: 0.0001
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 2
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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: `0.5.0`
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```yaml
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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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: ./dataforge
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type: chat_template
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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# chat_template: chatml
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.1
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output_dir: ./outputs/qwen05B
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unfrozen_parameters:
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- ^lm_head.weight$
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- ^model.embed_tokens.weight$
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# mlp.down_proj layers
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- model.layers.0.mlp.down_proj
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- model.layers.23.mlp.down_proj
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- model.layers.1.mlp.down_proj
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- model.layers.16.mlp.down_proj
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- model.layers.4.mlp.down_proj
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- model.layers.17.mlp.down_proj
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# mlp.gate_proj layers
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- model.layers.0.mlp.gate_proj
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- model.layers.1.mlp.gate_proj
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- model.layers.2.mlp.gate_proj
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- model.layers.3.mlp.gate_proj
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- model.layers.4.mlp.gate_proj
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- model.layers.7.mlp.gate_proj
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# mlp.up_proj layers
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- model.layers.1.mlp.up_proj
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- model.layers.0.mlp.up_proj
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- model.layers.3.mlp.up_proj
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- model.layers.4.mlp.up_proj
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- model.layers.7.mlp.up_proj
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- model.layers.9.mlp.up_proj
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# self_attn.k_proj layers
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- model.layers.18.self_attn.k_proj
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- model.layers.7.self_attn.k_proj
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- model.layers.19.self_attn.k_proj
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- model.layers.2.self_attn.k_proj
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- model.layers.6.self_attn.k_proj
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- model.layers.9.self_attn.k_proj
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# self_attn.o_proj layers
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- model.layers.16.self_attn.o_proj
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- model.layers.19.self_attn.o_proj
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- model.layers.0.self_attn.o_proj
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- model.layers.20.self_attn.o_proj
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- model.layers.4.self_attn.o_proj
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- model.layers.3.self_attn.o_proj
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# self_attn.q_proj layers
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- model.layers.13.self_attn.q_proj
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- model.layers.16.self_attn.q_proj
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- model.layers.21.self_attn.q_proj
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- model.layers.11.self_attn.q_proj
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- model.layers.15.self_attn.q_proj
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- model.layers.6.self_attn.q_proj
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# self_attn.v_proj layers
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- model.layers.2.self_attn.v_proj
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- model.layers.3.self_attn.v_proj
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- model.layers.4.self_attn.v_proj
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- model.layers.5.self_attn.v_proj
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- model.layers.7.self_attn.v_proj
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- model.layers.8.self_attn.v_proj
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sequence_len: 4096
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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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wandb_project: axolotl
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wandb_entity:
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wandb_watch:
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wandb_name: qwen2.5-0.5B
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 4
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num_epochs: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 1.0e-04
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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:
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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: 5
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xformers_attention:
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flash_attention: true
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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_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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pad_token: "<|im_end|>"
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eos_token: "<|im_end|>"
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```
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</details><br>
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| No log | 0.0013 | 1 | 1.7855 |
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| 1.2567 | 0.2504 | 194 | 1.5639 |
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| 1.2551 | 0.5008 | 388 | 1.4980 |
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| 1.1845 | 0.7512 | 582 | 1.4501 |
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| 1.3178 | 1.0019 | 776 | 1.4252 |
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| 1.06 | 1.2523 | 970 | 1.4187 |
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| 1.0697 | 1.5027 | 1164 | 1.4116 |
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| 1.0362 | 1.7531 | 1358 | 1.4100 |
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### Framework versions
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- Transformers 4.46.2
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.20.3 |