Modelpublic

dphn/dolphin-2.8-mistral-7b-v02

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

241likes3.4kdownloads

Model Card

Dolphin 2.8 Mistral 7b v0.2 🐬

By Eric Hartford and Cognitive Computations

![Discord](https://discord.gg/cognitivecomputations) Discord: https://discord.gg/cognitivecomputations

My appreciation for the sponsors of Dolphin 2.8:

—Crusoe Cloud - provided excellent on-demand 10xL40S node
—Winston Sou - Along with a generous anonymous sponsor, donated a massive personally owned compute resource!
—Abacus AI - my employer and partner in many things.

This model is based on Mistral-7b-v0.2 a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. Thanks to @alpindale for converting / publishing.

The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths.

It took 3 days on 10x L40S provided by Crusoe Cloud

Dolphin-2.8 has a variety of instruction, conversational, and coding skills.

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 to 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 Apache 2.0. I grant permission for any use including commercial. Dolphin was trained on data generated from GPT4 among other models.

Evals

{
  "arc_challenge": {
    "acc,none": 0.5921501706484642,
    "acc_stderr,none": 0.014361097288449701,
    "acc_norm,none": 0.6339590443686007,
    "acc_norm_stderr,none": 0.014077223108470139
  },
  "gsm8k": {
    "exact_match,strict-match": 0.4783927217589083,
    "exact_match_stderr,strict-match": 0.013759618667051773,
    "exact_match,flexible-extract": 0.5367702805155421,
    "exact_match_stderr,flexible-extract": 0.013735191956468648
  },
  "hellaswag": {
    "acc,none": 0.6389165504879506,
    "acc_stderr,none": 0.004793330525656218,
    "acc_norm,none": 0.8338976299541924,
    "acc_norm_stderr,none": 0.00371411888431746
  },
  "mmlu": {
    "acc,none": 0.6122347243982339,
    "acc_stderr,none": 0.003893774654142997
  },
  "truthfulqa_mc2": {
    "acc,none": 0.5189872652778472,
    "acc_stderr,none": 0.014901128316426086
  },
  "winogrande": {
    "acc,none": 0.7971586424625099,
    "acc_stderr,none": 0.011301439925936643
  }
}

<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.4.0

yaml


base_model: alpindale/Mistral-7B-v0.2-hf
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /workspace/datasets/dolphin201-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt.jsonl
    type: sharegpt
  - path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt.jsonl
    type: sharegpt
  - path: /workspace/datasets/not_samantha_norefusals.jsonl
    type: sharegpt
  - path: /workspace/datasets/openhermes2_5-sharegpt.jsonl
    type: sharegpt

chat_template: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /workspace/dolphin-2.8-mistral-7b

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: dolphin
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 3
num_epochs: 4
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.000005
optimizer: adamw_bnb_8bit

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10

eval_steps: 73
eval_table_size:
eval_table_max_new_tokens:
eval_sample_packing: false
saves_per_epoch: 
save_steps: 73
save_total_limit: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"

</details><br>

workspace/dolphin-2.8-mistral-7b

This model is a fine-tuned version of alpindale/Mistral-7B-v0.2-hf on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.4828

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: 5e-06
—trainbatchsize: 3
—evalbatchsize: 3
—seed: 42
—distributed_type: multi-GPU
—num_devices: 10
—gradientaccumulationsteps: 8
—totaltrainbatch_size: 240
—totalevalbatch_size: 30
—optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 10
—num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.1736	0.0	1	1.0338
0.6106	0.36	73	0.5439
0.5766	0.72	146	0.5171
0.5395	1.06	219	0.5045
0.5218	1.42	292	0.4976
0.5336	1.78	365	0.4915
0.5018	2.13	438	0.4885
0.5113	2.48	511	0.4856
0.5066	2.84	584	0.4838
0.4967	3.19	657	0.4834
0.4956	3.55	730	0.4830
0.5026	3.9	803	0.4828

Framework versions

—Transformers 4.40.0.dev0
—Pytorch 2.2.1+cu121
—Datasets 2.18.0
—Tokenizers 0.15.0

Quants

—dagbs/-GGUF

—bartowski/ExLlamaV2

—solidrust/AWQ