257 lines
7.5 KiB
Markdown
257 lines
7.5 KiB
Markdown
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---
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base_model: alpindale/Mistral-7B-v0.2-hf
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language:
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- en
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license: apache-2.0
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datasets:
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- cognitivecomputations/dolphin
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- cognitivecomputations/dolphin-coder
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- cognitivecomputations/samantha-data
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- jondurbin/airoboros-2.2.1
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- teknium/openhermes-2.5
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- m-a-p/Code-Feedback
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- m-a-p/CodeFeedback-Filtered-Instruction
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model-index:
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- name: dolphin-2.8-mistral-7b-v02
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results:
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- task:
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type: text-generation
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dataset:
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type: openai_humaneval
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name: HumanEval
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.469
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verified: false
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---
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# Dolphin 2.8 Mistral 7b v0.2 🐬
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By Eric Hartford and Cognitive Computations
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[](https://discord.gg/cognitivecomputations)
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Discord: https://discord.gg/cognitivecomputations
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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My appreciation for the sponsors of Dolphin 2.8:
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
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- [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource!
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- [Abacus AI](https://abacus.ai/) - my employer and partner in many things.
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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.
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The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths.
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It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/)
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Dolphin-2.8 has a variety of instruction, conversational, and coding skills.
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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.
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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.
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# Evals
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```
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{
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"arc_challenge": {
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"acc,none": 0.5921501706484642,
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"acc_stderr,none": 0.014361097288449701,
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"acc_norm,none": 0.6339590443686007,
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"acc_norm_stderr,none": 0.014077223108470139
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},
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"gsm8k": {
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"exact_match,strict-match": 0.4783927217589083,
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"exact_match_stderr,strict-match": 0.013759618667051773,
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"exact_match,flexible-extract": 0.5367702805155421,
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"exact_match_stderr,flexible-extract": 0.013735191956468648
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},
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"hellaswag": {
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"acc,none": 0.6389165504879506,
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"acc_stderr,none": 0.004793330525656218,
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"acc_norm,none": 0.8338976299541924,
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"acc_norm_stderr,none": 0.00371411888431746
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},
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"mmlu": {
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"acc,none": 0.6122347243982339,
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"acc_stderr,none": 0.003893774654142997
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},
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"truthfulqa_mc2": {
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"acc,none": 0.5189872652778472,
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"acc_stderr,none": 0.014901128316426086
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},
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"winogrande": {
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"acc,none": 0.7971586424625099,
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"acc_stderr,none": 0.011301439925936643
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}
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}
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```
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[<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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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```yaml
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base_model: alpindale/Mistral-7B-v0.2-hf
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: /workspace/datasets/dolphin201-sharegpt2.jsonl
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type: sharegpt
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- path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
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type: sharegpt
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- path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
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type: sharegpt
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- path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt.jsonl
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type: sharegpt
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- path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt.jsonl
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type: sharegpt
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- path: /workspace/datasets/not_samantha_norefusals.jsonl
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type: sharegpt
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- path: /workspace/datasets/openhermes2_5-sharegpt.jsonl
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type: sharegpt
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chat_template: chatml
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.001
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output_dir: /workspace/dolphin-2.8-mistral-7b
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sequence_len: 16384
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: dolphin
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wandb_entity:
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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gradient_accumulation_steps: 8
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micro_batch_size: 3
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num_epochs: 4
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adam_beta2: 0.95
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adam_epsilon: 0.00001
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max_grad_norm: 1.0
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lr_scheduler: cosine
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learning_rate: 0.000005
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optimizer: adamw_bnb_8bit
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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tf32: false
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 10
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eval_steps: 73
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eval_table_size:
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eval_table_max_new_tokens:
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eval_sample_packing: false
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saves_per_epoch:
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save_steps: 73
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save_total_limit: 2
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debug:
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deepspeed: deepspeed_configs/zero3_bf16.json
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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special_tokens:
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eos_token: "<|im_end|>"
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tokens:
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- "<|im_start|>"
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```
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</details><br>
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# workspace/dolphin-2.8-mistral-7b
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.4828
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-06
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- train_batch_size: 3
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- eval_batch_size: 3
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 10
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 240
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- total_eval_batch_size: 30
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.1736 | 0.0 | 1 | 1.0338 |
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| 0.6106 | 0.36 | 73 | 0.5439 |
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| 0.5766 | 0.72 | 146 | 0.5171 |
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| 0.5395 | 1.06 | 219 | 0.5045 |
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| 0.5218 | 1.42 | 292 | 0.4976 |
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| 0.5336 | 1.78 | 365 | 0.4915 |
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| 0.5018 | 2.13 | 438 | 0.4885 |
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| 0.5113 | 2.48 | 511 | 0.4856 |
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| 0.5066 | 2.84 | 584 | 0.4838 |
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| 0.4967 | 3.19 | 657 | 0.4834 |
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| 0.4956 | 3.55 | 730 | 0.4830 |
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| 0.5026 | 3.9 | 803 | 0.4828 |
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### Framework versions
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- Transformers 4.40.0.dev0
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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# Quants
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- [dagbs/-GGUF](https://huggingface.co/dagbs/dolphin-2.8-mistral-7b-v02-GGUF)
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- [bartowski/ExLlamaV2](https://huggingface.co/bartowski/dolphin-2.8-mistral-7b-v02-exl2)
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- [solidrust/AWQ](https://huggingface.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ)
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