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README.md

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+
+---
+
+license: llama3
+base_model: meta-llama/Meta-Llama-3-8B
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- Magpie-Align/Llama-3-8B-Self-Instruct-100K
+model-index:
+- name: Llama-3-8B-Self-Instruct-100K
+  results: []
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Llama-3-8B-Self-Instruct-100K-GGUF
+This is quantized version of [Magpie-Align/Llama-3-8B-Self-Instruct-100K](https://huggingface.co/Magpie-Align/Llama-3-8B-Self-Instruct-100K) created using llama.cpp
+
+# Original Model Card
+
+
+<!-- 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: `0.4.1`
+```yaml
+base_model: meta-llama/Meta-Llama-3-8B
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+chat_template: llama3
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: Magpie-Align/Llama-3-8B-Self-Instruct-100K
+    type: sharegpt
+    conversation: llama3
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.001
+output_dir: axolotl_out/Llama-3-8B-self-instruct-100K
+
+sequence_len: 8192
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project: SynDa
+wandb_entity:
+wandb_watch:
+wandb_name: Llama-3-8B-Self-Instruct
+wandb_log_model:
+hub_model_id: Magpie-Align/Llama-3-8B-Self-Instruct-100K
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 2
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 5
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>
+
+```
+
+</details><br>
+
+# Llama-3-8B-Self-Instruct-100K
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the Magpie-Align/Llama-3-8B-Self-Instruct-100K dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6245
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.3442        | 0.0190 | 1    | 2.3110          |
+| 0.9581        | 0.2095 | 11   | 1.1476          |
+| 0.8258        | 0.4190 | 22   | 0.9256          |
+| 0.717         | 0.6286 | 33   | 0.7341          |
+| 0.6746        | 0.8381 | 44   | 0.6497          |
+| 0.5601        | 1.0333 | 55   | 0.6268          |
+| 0.5571        | 1.2429 | 66   | 0.6285          |
+| 0.538         | 1.4524 | 77   | 0.6258          |
+| 0.548         | 1.6619 | 88   | 0.6251          |
+| 0.5467        | 1.8714 | 99   | 0.6245          |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1
+