Create README.md

README.md

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+---
+license: llama3
+base_model: Magpie-Align/Llama-3-8B-WildChat
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: Llama-3-8B-WildChat
+  results: []
+pipeline_tag: text-generation
+---
+
+# QuantFactory/Llama-3-8B-WildChat-GGUF
+This is quantized version of [Magpie-Align/Llama-3-8B-WildChat](https://huggingface.co/Magpie-Align/Llama-3-8B-WildChat) created using llama.cpp
+
+# Model Description
+[<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)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+
+base_model: meta-llama/Meta-Llama-3-8B
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: flydust/WildChat_ShareGPT
+    type: sharegpt
+    conversation: llama3
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.001
+output_dir: ./out_Llama-3-WildChat
+
+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-WildChat
+wandb_log_model:
+hub_model_id: SynDa/Llama-3-8B-WildChat
+
+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_steps: 100
+evals_per_epoch: 3
+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-WildChat
+
+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 None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8197
+
+## 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: 100
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.1455        | 0.0003 | 1    | 1.3389          |
+| 0.9084        | 0.3333 | 1128 | 0.8551          |
+| 0.9265        | 0.6667 | 2256 | 0.8363          |
+| 0.9086        | 1.0    | 3384 | 0.8210          |
+| 0.8257        | 1.3164 | 4512 | 0.8214          |
+| 0.8306        | 1.6497 | 5640 | 0.8197          |
+| 0.8252        | 1.9831 | 6768 | 0.8197          |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1