Phi-3-mini-4k-instruct-Friendly

cowWhySo/Phi-3-mini-4k-instruct-Friendly

Phi-3-mini-4k-instruct-Friendly is a text generation model from cowWhySo. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.

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22.9 GB on disk

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Hugging Face

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At a glance

Task: Text Generation
Library: transformers
License: mit
Model type: phi3
Access: Public
Created: Jun 5, 2024
Updated: Jun 7, 2024
SHA: 7f9abdc8

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Datasets

mlabonne/orpo-dpo-mix-40k

Model card

From the Hugging Face model README

This is a uncenscored version of Phi-3.

Abliterated using the following the guide here: https://huggingface.co/blog/mlabonne/abliteration

Then it was fine tuned on orpo-dpo-mix-40k

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: cowWhySo/Phi-3-mini-4k-instruct-Friendly
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: phi_3

load_in_8bit: false
load_in_4bit: true
strict: false
save_safetensors: true

rl: dpo
datasets:
  - path: mlabonne/orpo-dpo-mix-40k
    split: train
    type: chatml.intel

dataset_prepared_path:
val_set_size: 0.0
output_dir: ./out

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false

adapter: qlora
lora_model_dir:

lora_r: 64
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: phi3-mini-4k-instruct-Friendly
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false

bf16: auto

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: 150
evals_per_epoch: 0
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3.json
weight_decay: 0.01
max_grad_norm: 1.0
resize_token_embeddings_to_32x: true

</details><br>

Quants

GGUF: https://huggingface.co/cowWhySo/Phi-3-mini-4k-instruct-Friendly-gguf

Benchmarks

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Phi-3-mini-4k-instruct-Friendly	41	67.56	46.36	39.3	48.56

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	22.05	±	2.61
		acc_norm	22.05	±	2.61
agieval_logiqa_en	0	acc	41.01	±	1.93
		acc_norm	41.32	±	1.93
agieval_lsat_ar	0	acc	22.17	±	2.75
		acc_norm	22.17	±	2.75
agieval_lsat_lr	0	acc	45.69	±	2.21
		acc_norm	45.88	±	2.21
agieval_lsat_rc	0	acc	59.48	±	3.00
		acc_norm	56.51	±	3.03
agieval_sat_en	0	acc	75.24	±	3.01
		acc_norm	70.39	±	3.19
agieval_sat_en_without_passage	0	acc	39.81	±	3.42
		acc_norm	37.86	±	3.39
agieval_sat_math	0	acc	33.64	±	3.19
		acc_norm	31.82	±	3.15

Average: 41.0%

GPT4All

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	49.74	±	1.46
		acc_norm	50.43	±	1.46
arc_easy	0	acc	76.68	±	0.87
		acc_norm	73.23	±	0.91
boolq	1	acc	79.27	±	0.71
hellaswag	0	acc	57.91	±	0.49
		acc_norm	77.13	±	0.42
openbookqa	0	acc	35.00	±	2.14
		acc_norm	43.80	±	2.22
piqa	0	acc	77.86	±	0.97
		acc_norm	79.54	±	0.94
winogrande	0	acc	69.53	±	1.29

Average: 67.56%

TruthfulQA

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	31.21	±	1.62
		mc2	46.36	±	1.55

Average: 46.36%

Bigbench

Task	Version	Metric	Value		Stderr
bigbench_causal_judgement	0	multiple_choice_grade	54.74	±	3.62
bigbench_date_understanding	0	multiple_choice_grade	66.67	±	2.46
bigbench_disambiguation_qa	0	multiple_choice_grade	29.46	±	2.84
bigbench_geometric_shapes	0	multiple_choice_grade	11.98	±	1.72
		exact_str_match	0.00	±	0.00
bigbench_logical_deduction_five_objects	0	multiple_choice_grade	28.00	±	2.01
bigbench_logical_deduction_seven_objects	0	multiple_choice_grade	17.14	±	1.43
bigbench_logical_deduction_three_objects	0	multiple_choice_grade	45.67	±	2.88
bigbench_movie_recommendation	0	multiple_choice_grade	24.40	±	1.92
bigbench_navigate	0	multiple_choice_grade	53.70	±	1.58
bigbench_reasoning_about_colored_objects	0	multiple_choice_grade	68.10	±	1.04
bigbench_ruin_names	0	multiple_choice_grade	31.03	±	2.19
bigbench_salient_translation_error_detection	0	multiple_choice_grade	15.93	±	1.16
bigbench_snarks	0	multiple_choice_grade	77.35	±	3.12
bigbench_sports_understanding	0	multiple_choice_grade	52.64	±	1.59
bigbench_temporal_sequences	0	multiple_choice_grade	51.50	±	1.58
bigbench_tracking_shuffled_objects_five_objects	0	multiple_choice_grade	19.52	±	1.12
bigbench_tracking_shuffled_objects_seven_objects	0	multiple_choice_grade	13.89	±	0.83
bigbench_tracking_shuffled_objects_three_objects	0	multiple_choice_grade	45.67	±	2.88

Average: 39.3%

Average score: 48.56%

Training Summary

{
  "train/loss": 0.299,
  "train/grad_norm": 0.9337566701340533,
  "train/learning_rate": 0,
  "train/rewards/chosen": 0.08704188466072083,
  "train/rewards/rejected": -2.835820436477661,
  "train/rewards/accuracies": 0.84375,
  "train/rewards/margins": 2.9228620529174805,
  "train/logps/rejected": -509.9840393066406,
  "train/logps/chosen": -560.8234252929688,
  "train/logits/rejected": 1.6356163024902344,
  "train/logits/chosen": 1.7323706150054932,
  "train/epoch": 1.002169197396963,
  "train/global_step": 231,
  "_timestamp": 1717711643.3345022,
  "_runtime": 22808.557655334473,
  "_step": 231,
  "train_runtime": 22809.152,
  "train_samples_per_second": 1.944,
  "train_steps_per_second": 0.01,
  "total_flos": 0,
  "train_loss": 0.44557410065745895,
  "_wandb": {
    "runtime": 22810
  }
}

Task: Text Generation
Library: transformers
Type: phi3
License: mit
Created: Jun 5, 2024
Updated: Jun 7, 2024