--- license: other base_model: meta-llama/Meta-Llama-3-8B tags: - generated_from_trainer - axolotl model-index: - name: out results: [] datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - HuggingFaceH4/ultrachat_200k - microsoft/orca-math-word-problems-200k - abacusai/SystemChat-1.1 - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9 Llama 3 8b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations [![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations) Discord: https://discord.gg/cognitivecomputations A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it." My appreciation for the sponsors of Dolphin 2.9: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE) The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length. It took 2.5 days on 8x L40S provided by Crusoe Cloud This model was trained FFT on all parameters, using ChatML prompt template format. example: ``` <|im_start|>system You are Dolphin, a helpful AI assistant.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. 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 with 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 according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models. [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: meta-llama/Meta-Llama-3-8B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer tokenizer_use_fast: false load_in_8bit: false load_in_4bit: false strict: false model_config: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy val_set_size: 0.0002 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true gradient_accumulation_steps: 4 micro_batch_size: 3 num_epochs: 3 logging_steps: 1 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 2e-5 wandb_project: dolphin-2.9-mixtral-8x22b wandb_watch: wandb_run_id: wandb_log_model: train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true saves_per_epoch: 4 save_total_limit: 2 save_steps: evals_per_epoch: 4 eval_sample_packing: false debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" pad_token: "<|end_of_text|>" tokens: - "<|im_start|>" - "<|im_end|>" ```

## Quants GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 7 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.146 | 0.0005 | 1 | 1.1064 | | 0.6962 | 0.2501 | 555 | 0.6636 | | 0.6857 | 0.5001 | 1110 | 0.6503 | | 0.6592 | 0.7502 | 1665 | 0.6419 | | 0.6465 | 1.0002 | 2220 | 0.6317 | | 0.5295 | 1.2395 | 2775 | 0.6408 | | 0.5302 | 1.4895 | 3330 | 0.6351 | | 0.5188 | 1.7396 | 3885 | 0.6227 | | 0.521 | 1.9896 | 4440 | 0.6168 | | 0.3968 | 2.2289 | 4995 | 0.6646 | | 0.3776 | 2.4789 | 5550 | 0.6619 | | 0.3983 | 2.7290 | 6105 | 0.6602 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1