--- license: llama3 base_model: meta-llama/Meta-Llama-3-70B 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 - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Llama 3 70b 🐬 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

We have retrained our LLama-3-70b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be *too* reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9. Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40S node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Llama-3-70b, 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 3 days on an 8x H100 provided by Crusoe Cloud This model was trained FFT on parameters selected by [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py), 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-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We 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. We 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. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/NnLaOrgAud-D_L2QEOHz4.png) ## Training [

](https://github.com/OpenAccess-AI-Collective/axolotl)

See axolotl config

axolotl version: `0.4.0` ```yaml base_model: meta-llama/Meta-Llama-3-70B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false # load_in_4bit: true strict: false datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.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 chat_template: chatml # adapter: qlora # lora_r: 128 # lora_alpha: 16 # lora_modules_to_save: [embed_tokens, lm_head] # lora_dropout: 0.05 # lora_target_linear: true unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ # mlp.down_proj layers - model.layers.40.mlp.down_proj - model.layers.44.mlp.down_proj - model.layers.45.mlp.down_proj - model.layers.46.mlp.down_proj - model.layers.43.mlp.down_proj - model.layers.52.mlp.down_proj - model.layers.47.mlp.down_proj - model.layers.48.mlp.down_proj - model.layers.39.mlp.down_proj - model.layers.49.mlp.down_proj - model.layers.38.mlp.down_proj - model.layers.53.mlp.down_proj - model.layers.41.mlp.down_proj - model.layers.35.mlp.down_proj - model.layers.51.mlp.down_proj - model.layers.42.mlp.down_proj - model.layers.37.mlp.down_proj - model.layers.50.mlp.down_proj - model.layers.60.mlp.down_proj - model.layers.76.mlp.down_proj - model.layers.54.mlp.down_proj - model.layers.36.mlp.down_proj - model.layers.57.mlp.down_proj - model.layers.56.mlp.down_proj - model.layers.55.mlp.down_proj - model.layers.77.mlp.down_proj - model.layers.59.mlp.down_proj - model.layers.61.mlp.down_proj - model.layers.58.mlp.down_proj - model.layers.65.mlp.down_proj - model.layers.75.mlp.down_proj - model.layers.64.mlp.down_proj - model.layers.62.mlp.down_proj - model.layers.68.mlp.down_proj - model.layers.19.mlp.down_proj - model.layers.66.mlp.down_proj # mlp.gate_proj layers - model.layers.70.mlp.gate_proj - model.layers.71.mlp.gate_proj - model.layers.67.mlp.gate_proj - model.layers.58.mlp.gate_proj - model.layers.55.mlp.gate_proj - model.layers.57.mlp.gate_proj - model.layers.56.mlp.gate_proj - model.layers.66.mlp.gate_proj - model.layers.72.mlp.gate_proj - model.layers.69.mlp.gate_proj - model.layers.52.mlp.gate_proj - model.layers.54.mlp.gate_proj - model.layers.62.mlp.gate_proj - model.layers.60.mlp.gate_proj - model.layers.74.mlp.gate_proj - model.layers.59.mlp.gate_proj - model.layers.68.mlp.gate_proj - model.layers.61.mlp.gate_proj - model.layers.73.mlp.gate_proj - model.layers.53.mlp.gate_proj - model.layers.51.mlp.gate_proj - model.layers.63.mlp.gate_proj - model.layers.48.mlp.gate_proj - model.layers.49.mlp.gate_proj - model.layers.64.mlp.gate_proj - model.layers.50.mlp.gate_proj - model.layers.65.mlp.gate_proj - model.layers.47.mlp.gate_proj - model.layers.44.mlp.gate_proj - model.layers.45.mlp.gate_proj - model.layers.75.mlp.gate_proj - model.layers.46.mlp.gate_proj - model.layers.43.mlp.gate_proj - model.layers.77.mlp.gate_proj - model.layers.41.mlp.gate_proj - model.layers.42.mlp.gate_proj # mlp.up_proj layers - model.layers.70.mlp.up_proj - model.layers.67.mlp.up_proj - model.layers.66.mlp.up_proj - model.layers.69.mlp.up_proj - model.layers.62.mlp.up_proj - model.layers.65.mlp.up_proj - model.layers.63.mlp.up_proj - model.layers.68.mlp.up_proj - model.layers.71.mlp.up_proj - model.layers.64.mlp.up_proj - model.layers.61.mlp.up_proj - model.layers.58.mlp.up_proj - model.layers.59.mlp.up_proj - model.layers.57.mlp.up_proj - model.layers.55.mlp.up_proj - model.layers.72.mlp.up_proj - model.layers.54.mlp.up_proj - model.layers.60.mlp.up_proj - model.layers.56.mlp.up_proj - model.layers.73.mlp.up_proj - model.layers.50.mlp.up_proj - model.layers.51.mlp.up_proj - model.layers.53.mlp.up_proj - model.layers.74.mlp.up_proj - model.layers.52.mlp.up_proj - model.layers.49.mlp.up_proj - model.layers.30.mlp.up_proj - model.layers.34.mlp.up_proj - model.layers.47.mlp.up_proj - model.layers.46.mlp.up_proj - model.layers.48.mlp.up_proj - model.layers.38.mlp.up_proj - model.layers.45.mlp.up_proj - model.layers.43.mlp.up_proj - model.layers.29.mlp.up_proj - model.layers.42.mlp.up_proj # self_attn.k_proj layers - model.layers.72.self_attn.k_proj - model.layers.75.self_attn.k_proj - model.layers.71.self_attn.k_proj - model.layers.74.self_attn.k_proj - model.layers.44.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.33.self_attn.k_proj - model.layers.34.self_attn.k_proj - model.layers.76.self_attn.k_proj - model.layers.78.self_attn.k_proj - model.layers.77.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.18.self_attn.k_proj - model.layers.60.self_attn.k_proj - model.layers.17.self_attn.k_proj - model.layers.56.self_attn.k_proj - model.layers.2.self_attn.k_proj - model.layers.21.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.52.self_attn.k_proj - model.layers.35.self_attn.k_proj - model.layers.73.self_attn.k_proj - model.layers.15.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.36.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.37.self_attn.k_proj - model.layers.30.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.32.self_attn.k_proj - model.layers.41.self_attn.k_proj - model.layers.26.self_attn.k_proj # self_attn.o_proj layers - model.layers.50.self_attn.o_proj - model.layers.61.self_attn.o_proj - model.layers.46.self_attn.o_proj - model.layers.53.self_attn.o_proj - model.layers.54.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.42.self_attn.o_proj - model.layers.49.self_attn.o_proj - model.layers.41.self_attn.o_proj - model.layers.68.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.45.self_attn.o_proj - model.layers.11.self_attn.o_proj - model.layers.67.self_attn.o_proj - model.layers.48.self_attn.o_proj - model.layers.51.self_attn.o_proj - model.layers.64.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.47.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.20.self_attn.o_proj - model.layers.63.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.5.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.52.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.62.self_attn.o_proj - model.layers.56.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.6.self_attn.o_proj - model.layers.7.self_attn.o_proj # self_attn.q_proj layers - model.layers.2.self_attn.q_proj - model.layers.4.self_attn.q_proj - model.layers.46.self_attn.q_proj - model.layers.5.self_attn.q_proj - model.layers.7.self_attn.q_proj - model.layers.6.self_attn.q_proj - model.layers.9.self_attn.q_proj - model.layers.10.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.18.self_attn.q_proj - model.layers.62.self_attn.q_proj - model.layers.8.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.16.self_attn.q_proj - model.layers.31.self_attn.q_proj - model.layers.19.self_attn.q_proj - model.layers.17.self_attn.q_proj - model.layers.33.self_attn.q_proj - model.layers.35.self_attn.q_proj - model.layers.12.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.34.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.56.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.52.self_attn.q_proj - model.layers.54.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.30.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.29.self_attn.q_proj - model.layers.37.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.75.self_attn.q_proj # self_attn.v_proj layers - model.layers.11.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.37.self_attn.v_proj - model.layers.40.self_attn.v_proj - model.layers.41.self_attn.v_proj - model.layers.42.self_attn.v_proj - model.layers.43.self_attn.v_proj - model.layers.44.self_attn.v_proj - model.layers.45.self_attn.v_proj - model.layers.46.self_attn.v_proj - model.layers.48.self_attn.v_proj - model.layers.49.self_attn.v_proj - model.layers.50.self_attn.v_proj - model.layers.51.self_attn.v_proj - model.layers.53.self_attn.v_proj - model.layers.54.self_attn.v_proj - model.layers.55.self_attn.v_proj - model.layers.57.self_attn.v_proj - model.layers.58.self_attn.v_proj - model.layers.59.self_attn.v_proj - model.layers.60.self_attn.v_proj - model.layers.61.self_attn.v_proj - model.layers.62.self_attn.v_proj - model.layers.63.self_attn.v_proj - model.layers.64.self_attn.v_proj - model.layers.65.self_attn.v_proj - model.layers.66.self_attn.v_proj - model.layers.67.self_attn.v_proj - model.layers.69.self_attn.v_proj - model.layers.75.self_attn.v_proj - model.layers.18.self_attn.v_proj - model.layers.78.self_attn.v_proj - model.layers.68.self_attn.v_proj - model.layers.47.self_attn.v_proj - model.layers.38.self_attn.v_proj - model.layers.71.self_attn.v_proj # model.norm layers dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: /workspace/axolotl/llama-70b sequence_len: 4096 sample_packing: true pad_to_sequence_len: true wandb_project: llama-3 wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 5 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.00 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" pad_token: "<|end_of_text|>" tokens: - "<|im_start|>" - "<|im_end|>" ```

# workspace/axolotl/llama-70b This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4808 ## 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.7659 | 0.0004 | 1 | 0.7454 | | 0.5006 | 0.2501 | 587 | 0.4817 | | 0.4807 | 0.5002 | 1174 | 0.4698 | | 0.4758 | 0.7503 | 1761 | 0.4627 | | 0.4969 | 1.0004 | 2348 | 0.4558 | | 0.3604 | 1.2346 | 2935 | 0.4635 | | 0.3817 | 1.4847 | 3522 | 0.4572 | | 0.377 | 1.7348 | 4109 | 0.4533 | | 0.3695 | 1.9849 | 4696 | 0.4487 | | 0.2676 | 2.2187 | 5283 | 0.4825 | | 0.255 | 2.4688 | 5870 | 0.4814 | | 0.2851 | 2.7189 | 6457 | 0.4808 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1