End of training

README.md

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+---
+base_model: meta-llama/Meta-Llama-3-8B
+library_name: peft
+license: llama3
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: Sanskrit-llama
+  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: `0.4.1`
+```yaml
+
+base_model: meta-llama/Meta-Llama-3-8B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+max_steps: 
+bnb_config_kwargs:
+  llm_int8_has_fp16_weight: false
+  bnb_4bit_quant_type: nf4
+  bnb_4bit_use_double_quant: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: VinitT/Sanskrit-Llama_Base-Dataset
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0
+output_dir: ./outputs/qlora-out
+chat_template: chatml
+hub_model_id: VinitT/Sanskrit-llama
+hf_use_auth_token: true
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 512
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out: 
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+cosine_min_lr_ratio: 0.2
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: false
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: false
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+#fsdp:
+#  - full_shard
+#  - auto_wrap
+#fsdp_config:
+#  fsdp_limit_all_gathers: true
+#  fsdp_sync_module_states: true
+#  fsdp_offload_params: true
+#  fsdp_use_orig_params: false
+#  fsdp_cpu_ram_efficient_loading: true
+#  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+#  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+#  fsdp_state_dict_type: FULL_STATE_DICT
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+```
+
+</details><br>
+
+# Sanskrit-llama
+
+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.
+
+## 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: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- total_eval_batch_size: 2
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 1
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.42.4
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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