Create README.md

README.md

@@ -0,0 +1,274 @@
+---
+license: cc-by-nc-4.0
+base_model: mlabonne/NeuralMonarch-7B
+tags:
+- generated_from_trainer
+- axolotl
+- mistral
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+model-index:
+- name: AlphaMonarch-laser
+  results: []
+datasets:
+- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+<!-- 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. -->
+
+# AlphaMonarch-laser 
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/62S_ExHO6NKCM3NhPDrds.jpeg)
+
+AlphaMonarch-laser is a new DPO merge using laserQLoRA that retains all the reasoning abilities of the very best merges and significantly improves its 
+conversational abilities. Kind of the best of both worlds in a 7B model. This model uses [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B)
+as its base model, finetuned on only half of the layers using laserQLoRA. The preference dataset used for DPO is [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha).
+
+
+* [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B)
+* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
+
+
+</details><br>
+
+This model is a laserQLoRA fine-tuned version of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B).
+
+
+## Evaluation data
+
+🏆 Evaluation
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/MIkOaXVGJ0T5UVYIEhtYA.png)
+
+
+|    Task     |Version| Metric |Value|   |Stderr|
+|-------------|------:|--------|----:|---|-----:|
+|arc_challenge|      0|acc     |70.30|±  |  1.33|
+|             |       |acc_norm|73.12|±  |  1.29|
+|hellaswag    |      0|acc     |71.80|±  |  0.44|
+|             |       |acc_norm|89.20|±  |  0.30|
+|gsm8k        |      0|acc     |66.71|±  |  1.29|
+|winogrande   |      0|acc     |84.60|±  |  1.01|
+|mmlu         |      0|acc     |64.69|±  |  1.00|
+
+Average: 75.9% without mmlu
+
+### TruthfulQA
+|    Task     |Version|Metric|Value|   |Stderr|
+|-------------|------:|------|----:|---|-----:|
+|truthfulqa_mc|      1|mc1   |62.79|±  |  1.69|
+|             |       |mc2   |77.90|±  |  1.37|
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-07
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- training_steps: 1080
+
+
+### 📝 Axolotl Configuration
+
+```yaml
+base_model: mlabonne/NeuralMonarch-7B
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+rl: dpo
+chat_template: chatml
+datasets:
+  - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
+    split: train
+    type: chatml.intel
+dataset_prepared_path:
+val_set_size: 0.01
+output_dir: ./out
+adapter: qlora
+lora_model_dir:
+sequence_len: 1800
+sample_packing: false
+pad_to_sequence_len: false
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+ - layers.1.self_attn.q_proj
+ - layers.0.self_attn.q_proj
+ - layers.15.self_attn.q_proj
+ - layers.12.self_attn.q_proj
+ - layers.11.self_attn.q_proj
+ - layers.14.self_attn.q_proj
+ - layers.9.self_attn.q_proj
+ - layers.16.self_attn.q_proj
+ - layers.30.self_attn.q_proj
+ - layers.18.self_attn.q_proj
+ - layers.13.self_attn.q_proj
+ - layers.10.self_attn.q_proj
+ - layers.7.self_attn.q_proj
+ - layers.8.self_attn.q_proj
+ - layers.4.self_attn.q_proj
+ - layers.19.self_attn.q_proj
+ - layers.27.self_attn.k_proj
+ - layers.24.self_attn.k_proj
+ - layers.25.self_attn.k_proj
+ - layers.22.self_attn.k_proj
+ - layers.26.self_attn.k_proj
+ - layers.29.self_attn.k_proj
+ - layers.23.self_attn.k_proj
+ - layers.28.self_attn.k_proj
+ - layers.21.self_attn.k_proj
+ - layers.31.self_attn.k_proj
+ - layers.30.self_attn.k_proj
+ - layers.20.self_attn.k_proj
+ - layers.5.self_attn.k_proj
+ - layers.19.self_attn.k_proj
+ - layers.17.self_attn.k_proj
+ - layers.18.self_attn.k_proj
+ - layers.19.self_attn.v_proj
+ - layers.24.self_attn.v_proj
+ - layers.18.self_attn.v_proj
+ - layers.5.self_attn.v_proj
+ - layers.3.self_attn.v_proj
+ - layers.16.self_attn.v_proj
+ - layers.23.self_attn.v_proj
+ - layers.27.self_attn.v_proj
+ - layers.25.self_attn.v_proj
+ - layers.26.self_attn.v_proj
+ - layers.20.self_attn.v_proj
+ - layers.6.self_attn.v_proj
+ - layers.15.self_attn.v_proj
+ - layers.17.self_attn.v_proj
+ - layers.29.self_attn.v_proj
+ - layers.22.self_attn.v_proj
+ - layers.12.self_attn.o_proj
+ - layers.9.self_attn.o_proj
+ - layers.14.self_attn.o_proj
+ - layers.0.self_attn.o_proj
+ - layers.6.self_attn.o_proj
+ - layers.8.self_attn.o_proj
+ - layers.10.self_attn.o_proj
+ - layers.11.self_attn.o_proj
+ - layers.13.self_attn.o_proj
+ - layers.24.self_attn.o_proj
+ - layers.7.self_attn.o_proj
+ - layers.15.self_attn.o_proj
+ - layers.5.self_attn.o_proj
+ - layers.17.self_attn.o_proj
+ - layers.25.self_attn.o_proj
+ - layers.4.self_attn.o_proj
+ - layers.31.mlp.gate_proj
+ - layers.30.mlp.gate_proj
+ - layers.4.mlp.gate_proj
+ - layers.3.mlp.gate_proj
+ - layers.29.mlp.gate_proj
+ - layers.28.mlp.gate_proj
+ - layers.6.mlp.gate_proj
+ - layers.27.mlp.gate_proj
+ - layers.5.mlp.gate_proj
+ - layers.26.mlp.gate_proj
+ - layers.25.mlp.gate_proj
+ - layers.7.mlp.gate_proj
+ - layers.2.mlp.gate_proj
+ - layers.24.mlp.gate_proj
+ - layers.23.mlp.gate_proj
+ - layers.10.mlp.gate_proj
+ - layers.6.mlp.up_proj
+ - layers.4.mlp.up_proj
+ - layers.5.mlp.up_proj
+ - layers.27.mlp.up_proj
+ - layers.25.mlp.up_proj
+ - layers.26.mlp.up_proj
+ - layers.17.mlp.up_proj
+ - layers.24.mlp.up_proj
+ - layers.7.mlp.up_proj
+ - layers.10.mlp.up_proj
+ - layers.3.mlp.up_proj
+ - layers.11.mlp.up_proj
+ - layers.23.mlp.up_proj
+ - layers.9.mlp.up_proj
+ - layers.14.mlp.up_proj
+ - layers.18.mlp.up_proj
+ - layers.19.mlp.down_proj
+ - layers.20.mlp.down_proj
+ - layers.18.mlp.down_proj
+ - layers.21.mlp.down_proj
+ - layers.29.mlp.down_proj
+ - layers.1.mlp.down_proj
+ - layers.22.mlp.down_proj
+ - layers.28.mlp.down_proj
+ - layers.23.mlp.down_proj
+ - layers.30.mlp.down_proj
+ - layers.17.mlp.down_proj
+ - layers.4.mlp.down_proj
+ - layers.2.mlp.down_proj
+ - layers.15.mlp.down_proj
+ - layers.5.mlp.down_proj
+wandb_project: axolotl
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 5e-7
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+warmup_steps: 100
+evals_per_epoch: 1
+eval_table_size:
+eval_table_max_new_tokens: 128
+save_steps: 1080
+max_steps: 1080
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+```
+
+
+### Framework versions
+
+- Transformers 4.38.0.dev0
+- Pytorch 2.1.2+cu118
+- Datasets 2.17.0
+- Tokenizers 0.15.0
+- axolotl: 0.4.0
+
+[<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)