--- license: other base_model: meta-llama/Meta-Llama-3-8B tags: - generated_from_trainer 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 --- This is the [llamafile](https://github.com/Mozilla-Ocho/llamafile) for [Dolphin 2.9 Llama 3 8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b). Quick tests show it's good but not as sharp as the base model, using just some few shot prompts looking for precision when asking about history and science. More tests will have to be done to compare this and WizardLM-7B to see how much the finetuning/new EOS did to Llama-3-8B. Notably, [cognitivecomputations](https://huggingface.co/cognitivecomputations) uses a single EOS token. This fixes the garbled output bug. Hooray! It may however prevent some intended behavior of Llama3's internal monologue/thoughts that adds to the model's apparent sharpness. Download Meta's original weights and load manually in python to see what it's capable of as a comparison. We're all awaiting any fixes to llama.cpp and/or the base gguf structure. In the meantime this dolphin is a good fix and excellent work. conversion notes: I converted the original safetensors to f32 to preserve the fidelity from bf16, then quantized ggufs from there. Not sure what most ggufs on hf are doing if they don't say. size notes: Windows users, go for q3-k-m. Others, use the biggest one that works on your machine. FreeBSD users, you're the real heroes. I just copied the original model card this time. ## .-=~ Original Model Card ~=-. # Dolphin 2.9 Llama 3 8b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx 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|>" ```

## 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