--- license: apache-2.0 base_model: mistral-community/Mixtral-8x22B-v0.1 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 - abacusai/SystemChat-1.1 - Locutusque/function-calling-chatml - internlm/Agent-FLAN language: - en --- # Dolphin 2.9.1 Mixtral 1x22b 🐬 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

This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 16k sequence length. It took 27 hours on 8xH100 provided by Crusoe Cloud. This model was fully fine-tuned, targeting all layers. The model is an extracted expert using SLERP and a custom script that we've open-sourced. It extracts a single expert which is the combined SLERP of all 8 experts from a Mixtral architecture. We decided to not fully convert to a dense model, for the sake of trying to keep as much of the original model's performance as possible, as this process is already quite surgical and there are a lot of variables to take into account. 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. 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 under Apache 2.0. We grant permission for any use, including commercial, as long as it complies with the Apache-2.0 license. Dolphin was trained using data generated from GPT-4, among other models. For more details on the extraction process of the expert model, visit our GitHub repository: https://github.com/cognitivecomputations/extract-expert/tree/main ## Evals ![image/png](https://i.ibb.co/yNmCv76/file-nkvf-Q9-Mg-X57-GB7-Ayrl-YA2-Zsp.png) [

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

See axolotl config

axolotl version: `0.4.0` ```yaml base_model: cognitivecomputations/mixtral-1x22b-base model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # trust_remote_code: true # load_in_8bit: true # 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 dataset_prepared_path: yi34b-prepared val_set_size: 0.01 output_dir: ./1x22b-out # adapter: qlora # lora_r: 16 # 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$ # # input_layernorm layers # - model.layers.0.input_layernorm # - model.layers.1.input_layernorm # - model.layers.2.input_layernorm # - model.layers.3.input_layernorm # - model.layers.4.input_layernorm # - model.layers.5.input_layernorm # - model.layers.6.input_layernorm # - model.layers.7.input_layernorm # - model.layers.8.input_layernorm # - model.layers.9.input_layernorm # - model.layers.10.input_layernorm # - model.layers.11.input_layernorm # - model.layers.12.input_layernorm # - model.layers.13.input_layernorm # - model.layers.14.input_layernorm # - model.layers.15.input_layernorm # - model.layers.16.input_layernorm # - model.layers.17.input_layernorm # - model.layers.18.input_layernorm # - model.layers.19.input_layernorm # - model.layers.20.input_layernorm # - model.layers.21.input_layernorm # - model.layers.22.input_layernorm # - model.layers.23.input_layernorm # # lm_head layers # # mlp.down_proj layers # - model.layers.17.mlp.down_proj # - model.layers.18.mlp.down_proj # - model.layers.19.mlp.down_proj # - model.layers.20.mlp.down_proj # - model.layers.21.mlp.down_proj # - model.layers.22.mlp.down_proj # - model.layers.23.mlp.down_proj # - model.layers.24.mlp.down_proj # - model.layers.25.mlp.down_proj # - model.layers.26.mlp.down_proj # - model.layers.27.mlp.down_proj # - model.layers.28.mlp.down_proj # - model.layers.29.mlp.down_proj # - model.layers.30.mlp.down_proj # - model.layers.31.mlp.down_proj # - model.layers.32.mlp.down_proj # - model.layers.33.mlp.down_proj # - model.layers.34.mlp.down_proj # - model.layers.35.mlp.down_proj # - model.layers.36.mlp.down_proj # - model.layers.37.mlp.down_proj # - model.layers.38.mlp.down_proj # - model.layers.39.mlp.down_proj # - model.layers.40.mlp.down_proj # # mlp.gate_proj layers # - model.layers.51.mlp.gate_proj # - model.layers.50.mlp.gate_proj # - model.layers.53.mlp.gate_proj # - model.layers.52.mlp.gate_proj # - model.layers.49.mlp.gate_proj # - model.layers.45.mlp.gate_proj # - model.layers.46.mlp.gate_proj # - model.layers.47.mlp.gate_proj # - model.layers.57.mlp.gate_proj # - model.layers.48.mlp.gate_proj # - model.layers.56.mlp.gate_proj # - model.layers.41.mlp.gate_proj # - model.layers.54.mlp.gate_proj # - model.layers.43.mlp.gate_proj # - model.layers.44.mlp.gate_proj # - model.layers.60.mlp.gate_proj # - model.layers.55.mlp.gate_proj # - model.layers.40.mlp.gate_proj # - model.layers.42.mlp.gate_proj # - model.layers.58.mlp.gate_proj # - model.layers.36.mlp.gate_proj # - model.layers.37.mlp.gate_proj # - model.layers.38.mlp.gate_proj # - model.layers.39.mlp.gate_proj # # mlp.up_proj layers # - model.layers.50.mlp.up_proj # - model.layers.51.mlp.up_proj # - model.layers.41.mlp.up_proj # - model.layers.49.mlp.up_proj # - model.layers.43.mlp.up_proj # - model.layers.44.mlp.up_proj # - model.layers.40.mlp.up_proj # - model.layers.45.mlp.up_proj # - model.layers.47.mlp.up_proj # - model.layers.48.mlp.up_proj # - model.layers.46.mlp.up_proj # - model.layers.42.mlp.up_proj # - model.layers.39.mlp.up_proj # - model.layers.36.mlp.up_proj # - model.layers.37.mlp.up_proj # - model.layers.38.mlp.up_proj # - model.layers.56.mlp.up_proj # - model.layers.57.mlp.up_proj # - model.layers.53.mlp.up_proj # - model.layers.31.mlp.up_proj # - model.layers.32.mlp.up_proj # - model.layers.34.mlp.up_proj # - model.layers.35.mlp.up_proj # - model.layers.33.mlp.up_proj # # model.embed_tokens layers # # model.norm layers # # post_attention_layernorm layers # - model.layers.0.post_attention_layernorm # - model.layers.1.post_attention_layernorm # - model.layers.2.post_attention_layernorm # - model.layers.3.post_attention_layernorm # - model.layers.4.post_attention_layernorm # - model.layers.5.post_attention_layernorm # - model.layers.6.post_attention_layernorm # - model.layers.7.post_attention_layernorm # - model.layers.8.post_attention_layernorm # - model.layers.9.post_attention_layernorm # - model.layers.10.post_attention_layernorm # - model.layers.11.post_attention_layernorm # - model.layers.12.post_attention_layernorm # - model.layers.13.post_attention_layernorm # - model.layers.14.post_attention_layernorm # - model.layers.15.post_attention_layernorm # - model.layers.16.post_attention_layernorm # - model.layers.17.post_attention_layernorm # - model.layers.18.post_attention_layernorm # - model.layers.19.post_attention_layernorm # - model.layers.20.post_attention_layernorm # - model.layers.21.post_attention_layernorm # - model.layers.22.post_attention_layernorm # - model.layers.23.post_attention_layernorm # # self_attn.k_proj layers # - model.layers.42.self_attn.k_proj # - model.layers.41.self_attn.k_proj # - model.layers.39.self_attn.k_proj # - model.layers.35.self_attn.k_proj # - model.layers.28.self_attn.k_proj # - model.layers.79.self_attn.k_proj # - model.layers.43.self_attn.k_proj # - model.layers.32.self_attn.k_proj # - model.layers.73.self_attn.k_proj # - model.layers.31.self_attn.k_proj # - model.layers.29.self_attn.k_proj # - model.layers.76.self_attn.k_proj # - model.layers.30.self_attn.k_proj # - model.layers.40.self_attn.k_proj # - model.layers.33.self_attn.k_proj # - model.layers.78.self_attn.k_proj # - model.layers.34.self_attn.k_proj # - model.layers.37.self_attn.k_proj # - model.layers.45.self_attn.k_proj # - model.layers.44.self_attn.k_proj # - model.layers.71.self_attn.k_proj # - model.layers.26.self_attn.k_proj # - model.layers.74.self_attn.k_proj # - model.layers.27.self_attn.k_proj # # self_attn.o_proj layers # - model.layers.35.self_attn.o_proj # - model.layers.34.self_attn.o_proj # - model.layers.37.self_attn.o_proj # - model.layers.33.self_attn.o_proj # - model.layers.31.self_attn.o_proj # - model.layers.27.self_attn.o_proj # - model.layers.38.self_attn.o_proj # - model.layers.24.self_attn.o_proj # - model.layers.39.self_attn.o_proj # - model.layers.43.self_attn.o_proj # - model.layers.29.self_attn.o_proj # - model.layers.0.self_attn.o_proj # - model.layers.50.self_attn.o_proj # - model.layers.32.self_attn.o_proj # - model.layers.45.self_attn.o_proj # - model.layers.30.self_attn.o_proj # - model.layers.60.self_attn.o_proj # - model.layers.23.self_attn.o_proj # - model.layers.18.self_attn.o_proj # - model.layers.67.self_attn.o_proj # - model.layers.57.self_attn.o_proj # - model.layers.20.self_attn.o_proj # - model.layers.76.self_attn.o_proj # - model.layers.28.self_attn.o_proj # # self_attn.q_proj layers # - model.layers.1.self_attn.q_proj # - model.layers.6.self_attn.q_proj # - model.layers.0.self_attn.q_proj # - model.layers.5.self_attn.q_proj # - model.layers.2.self_attn.q_proj # - model.layers.7.self_attn.q_proj # - model.layers.3.self_attn.q_proj # - model.layers.4.self_attn.q_proj # - model.layers.8.self_attn.q_proj # - model.layers.9.self_attn.q_proj # - model.layers.61.self_attn.q_proj # - model.layers.10.self_attn.q_proj # - model.layers.62.self_attn.q_proj # - model.layers.36.self_attn.q_proj # - model.layers.15.self_attn.q_proj # - model.layers.11.self_attn.q_proj # - model.layers.17.self_attn.q_proj # - model.layers.60.self_attn.q_proj # - model.layers.63.self_attn.q_proj # - model.layers.64.self_attn.q_proj # - model.layers.29.self_attn.q_proj # - model.layers.30.self_attn.q_proj # - model.layers.55.self_attn.q_proj # - model.layers.34.self_attn.q_proj # # self_attn.v_proj layers # - model.layers.12.self_attn.v_proj # - model.layers.16.self_attn.v_proj # - model.layers.18.self_attn.v_proj # - model.layers.19.self_attn.v_proj # - model.layers.20.self_attn.v_proj # - model.layers.21.self_attn.v_proj # - model.layers.22.self_attn.v_proj # - model.layers.23.self_attn.v_proj # - model.layers.24.self_attn.v_proj # - model.layers.25.self_attn.v_proj # - model.layers.26.self_attn.v_proj # - model.layers.27.self_attn.v_proj # - model.layers.28.self_attn.v_proj # - model.layers.29.self_attn.v_proj # - model.layers.30.self_attn.v_proj # - model.layers.31.self_attn.v_proj # - model.layers.32.self_attn.v_proj # - model.layers.33.self_attn.v_proj # - model.layers.34.self_attn.v_proj # - model.layers.35.self_attn.v_proj # - model.layers.36.self_attn.v_proj # - model.layers.37.self_attn.v_proj # - model.layers.38.self_attn.v_proj # - model.layers.39.self_attn.v_proj sequence_len: 16384 sample_packing: true pad_to_sequence_len: true # adapter: lora # lora_model_dir: # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: true # lora_fan_in_fan_out: wandb_project: dolphin-mixtral1x22b wandb_entity: wandb_watch: wandb_name: 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: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: /workspace/axolotl2/axolotl/1x22b-out/checkpoint-507 local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 4 save_total_limit: 2 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" bos_token: "~~" # pad_token: "" unk_token: "" tokens: - "<|im_start|>" ```~~

# 1x22b-out ## 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: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.9818 | 0.0015 | 1 | 0.9854 | | 0.4783 | 0.2499 | 169 | 0.5042 | | 0.464 | 0.4997 | 338 | 0.4755 | | 0.4561 | 0.7496 | 507 | 0.4593 | | 0.3981 | 0.9994 | 676 | 0.4553 | | 0.3725 | 1.2378 | 845 | 0.4525 | | 0.3624 | 1.4877 | 1014 | 0.4457 | | 0.359 | 1.7376 | 1183 | 0.4393 | | 0.375 | 1.9874 | 1352 | 0.4345 | | 0.2899 | 2.2260 | 1521 | 0.4488 | | 0.2848 | 2.4759 | 1690 | 0.4473 | | 0.2935 | 2.7257 | 1859 | 0.4470 | | 0.2065 | 2.9756 | 2028 | 0.4572 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1