AiAF
/

Pretraining-Yellow-Atol-7B-Instruct-V1

@@ -1,202 +1,207 @@
 ---
-base_model: mistralai/Mistral-7B-Instruct-v0.3
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
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 ### Framework versions
-- PEFT 0.14.0

 ---
 library_name: peft
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- json
+model-index:
+- name: Pretraining-SpongeBoB-7B-Instruct-V1
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+[<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)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.8.0.dev0`
+```yaml
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+# optionally might have model_type or tokenizer_type
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+# Automatically upload checkpoint and final model to HF
+hub_model_id: AiAF/Pretraining-SpongeBoB-7B-Instruct-V1
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+datasets:
+  - path: json
+    data_files: [pretraining.jsonl]
+    type: completion
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out/Pretraining-SpongeBoB-7B-Instruct-V1
+save_total_limit: 10
+adapter: qlora
+lora_model_dir:
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+lora_r: 256
+lora_alpha: 64
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+wandb_project: "LLM-Pretraining"
+wandb_entity:
+wandb_watch: "all"
+wandb_name: "Pretraining-SpongeBoB-7B-Instruct-V1"
+wandb_run_id: "Pretraining-SpongeBoB-7B-Instruct-V1"
+wandb_log_model: "false"
+gradient_accumulation_steps: 2
+micro_batch_size: 9
+num_epochs: 10
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.000005
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+warmup_steps: 10
+evals_per_epoch: 5
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 5
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+```
+</details><br>
+# Pretraining-SpongeBoB-7B-Instruct-V1
+This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the json dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6255
+## 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: 5e-06
+- train_batch_size: 9
+- eval_batch_size: 9
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 18
+- 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: 10
+- num_epochs: 10.0
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.7843        | 0.0417 | 1    | 1.7939          |
+| 1.8262        | 0.2083 | 5    | 1.7915          |
+| 1.839         | 0.4167 | 10   | 1.7733          |
+| 1.7503        | 0.625  | 15   | 1.7438          |
+| 1.7191        | 0.8333 | 20   | 1.7260          |
+| 1.7191        | 1.0417 | 25   | 1.7138          |
+| 1.7548        | 1.25   | 30   | 1.7023          |
+| 1.6795        | 1.4583 | 35   | 1.6924          |
+| 1.6848        | 1.6667 | 40   | 1.6836          |
+| 1.6856        | 1.875  | 45   | 1.6770          |
+| 1.7155        | 2.0833 | 50   | 1.6715          |
+| 1.6901        | 2.2917 | 55   | 1.6665          |
+| 1.6797        | 2.5    | 60   | 1.6621          |
+| 1.6704        | 2.7083 | 65   | 1.6581          |
+| 1.6763        | 2.9167 | 70   | 1.6545          |
+| 1.678         | 3.125  | 75   | 1.6516          |
+| 1.6271        | 3.3333 | 80   | 1.6490          |
+| 1.662         | 3.5417 | 85   | 1.6468          |
+| 1.6384        | 3.75   | 90   | 1.6446          |
+| 1.6273        | 3.9583 | 95   | 1.6427          |
+| 1.5934        | 4.1667 | 100  | 1.6408          |
+| 1.6217        | 4.375  | 105  | 1.6393          |
+| 1.6383        | 4.5833 | 110  | 1.6378          |
+| 1.6244        | 4.7917 | 115  | 1.6365          |
+| 1.6238        | 5.0    | 120  | 1.6352          |
+| 1.6179        | 5.2083 | 125  | 1.6340          |
+| 1.6203        | 5.4167 | 130  | 1.6330          |
+| 1.6177        | 5.625  | 135  | 1.6319          |
+| 1.6332        | 5.8333 | 140  | 1.6310          |
+| 1.6277        | 6.0417 | 145  | 1.6302          |
+| 1.6461        | 6.25   | 150  | 1.6296          |
+| 1.6668        | 6.4583 | 155  | 1.6290          |
+| 1.6249        | 6.6667 | 160  | 1.6284          |
+| 1.6013        | 6.875  | 165  | 1.6278          |
+| 1.6098        | 7.0833 | 170  | 1.6274          |
+| 1.5954        | 7.2917 | 175  | 1.6270          |
+| 1.6488        | 7.5    | 180  | 1.6267          |
+| 1.6153        | 7.7083 | 185  | 1.6264          |
+| 1.6232        | 7.9167 | 190  | 1.6262          |
+| 1.6611        | 8.125  | 195  | 1.6260          |
+| 1.5997        | 8.3333 | 200  | 1.6258          |
+| 1.6166        | 8.5417 | 205  | 1.6258          |
+| 1.6427        | 8.75   | 210  | 1.6256          |
+| 1.6157        | 8.9583 | 215  | 1.6255          |
+| 1.6303        | 9.1667 | 220  | 1.6255          |
+| 1.6179        | 9.375  | 225  | 1.6255          |
+| 1.6063        | 9.5833 | 230  | 1.6255          |
+| 1.6043        | 9.7917 | 235  | 1.6255          |
+| 1.5881        | 10.0   | 240  | 1.6255          |
 ### Framework versions
+- PEFT 0.14.0
+- Transformers 4.49.0
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0