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README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: mistral-community/Mixtral-8x22B-v0.1
+model-index:
+- name: qlora-out-2048-multiling
+ 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/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: mistral-community/Mixtral-8x22B-v0.1
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+ - path: lightblue/gpt4_conversations_multilingual
+ type: sharegpt
+ conversation: mistral
+dataset_prepared_path: ./prepared_dataset_2048-multiling
+val_set_size: 0
+output_dir: ./qlora-out-2048-multiling
+
+## You can optionally freeze the entire model and unfreeze a subset of parameters
+unfrozen_parameters:
+# - ^lm_head.weight$
+# - ^model.embed_tokens.weight$[:32000]
+# - model.layers.2[0-9]+.block_sparse_moe.gate
+# - model.layers.2[0-9]+.block_sparse_moe.experts
+# - model.layers.3[0-9]+.block_sparse_moe.gate
+# - model.layers.3[0-9]+.block_sparse_moe.experts
+
+model_config:
+ output_router_logits: true
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+#lora_target_modules:
+# - gate
+# - q_proj
+# - k_proj
+# - v_proj
+# - o_proj
+# - w1
+# - w2
+# - w3
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+use_wandb: true
+wandb_project: axolotl
+wandb_entity: peterd
+wandb_name: mixtral_8x22b_test
+
+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
+
+warmup_steps: 10
+evals_per_epoch: 0
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 5
+debug:
+deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+```
+
+</details><br>
+
+# qlora-out-2048-multiling
+
+This model is a fine-tuned version of [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) 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: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- 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: 1
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.0.dev0
+- Pytorch 2.1.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.0