Nirman
dphn

dphn/dolphin-2.9.1-yi-1.5-34b

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Model Documentation

Dolphin 2.9.1 Yi 1.5 34b 🐬



Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations

This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream.

Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well.

Website: https://dphn.ai Twitter: https://x.com/dphnAI Web Chat: https://chat.dphn.ai Telegram bot: https://t.me/DolphinAI_bot



Our appreciation for the sponsors of Dolphin 2.9.1:
  • Crusoe Cloud - provided excellent on-demand 8xH100 node
  • OnDemand - provided inference sponsorship


    • This model is based on Yi-1.5-34b, and is governed by apache 2.0 license.

    The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length.

    Dolphin 2.9.1 uses 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 apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.

    Evals



    image/png

    Training



    Built with Axolotl
    See axolotl config

    axolotl version: 0.4.0 
    yaml
base_model: 01-ai/Yi-1.5-34B
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

    load_in_8bit: false
    

    load_in_4bit: true
    

    strict: false


    adapter: qlora
    

    lora_modules_to_save: [embed_tokens, lm_head]


    lora_r: 32
    

    lora_alpha: 16
    

    lora_dropout: 0.05
    

    lora_target_linear: True
    

    lora_fan_in_fan_out:


    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
val_set_size: 0.01
output_dir: ./out-yi

    sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

    wandb_project: dolphin-2.9-yi-34b
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: /workspace/axolotl/dbrx-checkpoint
    
logging_steps: 1
xformers_attention:
flash_attention: true

    warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
save_total_limit: 2
save_steps:
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|startoftext|>"
  eos_token: "<|im_end|>"
  pad_token: ""
  unk_token: ""
tokens:
  
  • "<|im_start|>"
    •






    out-yi



    This model is a fine-tuned version of 01-ai/Yi-1.5-34B on the None dataset. It achieves the following results on the evaluation set:
  • Loss: 0.4425


    • 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.6265 | 0.0 | 1 | 0.6035 | | 0.4674 | 0.25 | 327 | 0.4344 | | 0.4337 | 0.5 | 654 | 0.4250 | | 0.4346 | 0.75 | 981 | 0.4179 | | 0.3985 | 1.0 | 1308 | 0.4118 | | 0.3128 | 1.23 | 1635 | 0.4201 | | 0.3261 | 1.48 | 1962 | 0.4157 | | 0.3259 | 1.73 | 2289 | 0.4122 | | 0.3126 | 1.98 | 2616 | 0.4079 | | 0.2265 | 2.21 | 2943 | 0.4441 | | 0.2297 | 2.46 | 3270 | 0.4427 | | 0.2424 | 2.71 | 3597 | 0.4425 |

    Framework versions



  • Transformers 4.40.0.dev0
  • Pytorch 2.2.2+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0

    • Files & Weights

    FilenameSizeAction
    model-00001-of-00015.safetensors 4.46 GB
    model-00002-of-00015.safetensors 4.43 GB
    model-00003-of-00015.safetensors 4.65 GB
    model-00004-of-00015.safetensors 4.43 GB
    model-00005-of-00015.safetensors 4.43 GB
    model-00006-of-00015.safetensors 4.65 GB
    model-00007-of-00015.safetensors 4.43 GB
    model-00008-of-00015.safetensors 4.43 GB
    model-00009-of-00015.safetensors 4.65 GB
    model-00010-of-00015.safetensors 4.43 GB
    model-00011-of-00015.safetensors 4.43 GB
    model-00012-of-00015.safetensors 4.65 GB
    model-00013-of-00015.safetensors 4.43 GB
    model-00014-of-00015.safetensors 4.43 GB
    model-00015-of-00015.safetensors 1.13 GB