--- base_model: alpindale/Mistral-7B-v0.2-hf language: - en license: apache-2.0 datasets: - cognitivecomputations/dolphin - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - jondurbin/airoboros-2.2.1 - teknium/openhermes-2.5 - m-a-p/Code-Feedback - m-a-p/CodeFeedback-Filtered-Instruction model-index: - name: dolphin-2.8-mistral-7b-v02 results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.469 verified: false --- # Dolphin 2.8 Mistral 7b v0.2 🐬 By Eric Hartford and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx My appreciation for the sponsors of Dolphin 2.8: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node - [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource! - [Abacus AI](https://abacus.ai/) - my employer and partner in many things. This model is based on [Mistral-7b-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. Thanks to @alpindale for converting / publishing. The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths. It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/) Dolphin-2.8 has a variety of instruction, conversational, and coding skills. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use including commercial. Dolphin was trained on data generated from GPT4 among other models. # Evals ``` { "arc_challenge": { "acc,none": 0.5921501706484642, "acc_stderr,none": 0.014361097288449701, "acc_norm,none": 0.6339590443686007, "acc_norm_stderr,none": 0.014077223108470139 }, "gsm8k": { "exact_match,strict-match": 0.4783927217589083, "exact_match_stderr,strict-match": 0.013759618667051773, "exact_match,flexible-extract": 0.5367702805155421, "exact_match_stderr,flexible-extract": 0.013735191956468648 }, "hellaswag": { "acc,none": 0.6389165504879506, "acc_stderr,none": 0.004793330525656218, "acc_norm,none": 0.8338976299541924, "acc_norm_stderr,none": 0.00371411888431746 }, "mmlu": { "acc,none": 0.6122347243982339, "acc_stderr,none": 0.003893774654142997 }, "truthfulqa_mc2": { "acc,none": 0.5189872652778472, "acc_stderr,none": 0.014901128316426086 }, "winogrande": { "acc,none": 0.7971586424625099, "acc_stderr,none": 0.011301439925936643 } } ``` [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: alpindale/Mistral-7B-v0.2-hf model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: /workspace/datasets/dolphin201-sharegpt2.jsonl type: sharegpt - path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt - path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt - path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt.jsonl type: sharegpt - path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt.jsonl type: sharegpt - path: /workspace/datasets/not_samantha_norefusals.jsonl type: sharegpt - path: /workspace/datasets/openhermes2_5-sharegpt.jsonl type: sharegpt chat_template: chatml dataset_prepared_path: last_run_prepared val_set_size: 0.001 output_dir: /workspace/dolphin-2.8-mistral-7b sequence_len: 16384 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 3 num_epochs: 4 adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 lr_scheduler: cosine learning_rate: 0.000005 optimizer: adamw_bnb_8bit train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 eval_steps: 73 eval_table_size: eval_table_max_new_tokens: eval_sample_packing: false saves_per_epoch: save_steps: 73 save_total_limit: 2 debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.1 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" tokens: - "<|im_start|>" ```

# workspace/dolphin-2.8-mistral-7b This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4828 ## 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: 5e-06 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - distributed_type: multi-GPU - num_devices: 10 - gradient_accumulation_steps: 8 - total_train_batch_size: 240 - total_eval_batch_size: 30 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1736 | 0.0 | 1 | 1.0338 | | 0.6106 | 0.36 | 73 | 0.5439 | | 0.5766 | 0.72 | 146 | 0.5171 | | 0.5395 | 1.06 | 219 | 0.5045 | | 0.5218 | 1.42 | 292 | 0.4976 | | 0.5336 | 1.78 | 365 | 0.4915 | | 0.5018 | 2.13 | 438 | 0.4885 | | 0.5113 | 2.48 | 511 | 0.4856 | | 0.5066 | 2.84 | 584 | 0.4838 | | 0.4967 | 3.19 | 657 | 0.4834 | | 0.4956 | 3.55 | 730 | 0.4830 | | 0.5026 | 3.9 | 803 | 0.4828 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0 # Quants - [dagbs/-GGUF](https://huggingface.co/dagbs/dolphin-2.8-mistral-7b-v02-GGUF) - [bartowski/ExLlamaV2](https://huggingface.co/bartowski/dolphin-2.8-mistral-7b-v02-exl2) - [solidrust/AWQ](https://huggingface.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ)