koesn/Garrulus-7B-GGUF

Garrulus-7B

Description

This repo contains GGUF format model files for Garrulus-7B.

Files Provided

Name	Quant	Bits	File Size	Remark
garrulus-7b.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
garrulus-7b.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
garrulus-7b.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
garrulus-7b.Q4_0.gguf	IQ4_NL	4	4.11 GB	4.25 bpw non-linear quantization
garrulus-7b.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
garrulus-7b.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
garrulus-7b.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
garrulus-7b.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
garrulus-7b.Q8_0.gguf	Q8_0	8	7.70 GB	6.70G, +0.0004 ppl

Parameters

path	type	architecture	rope_theta	sliding_win	max_pos_embed
udkai/Garrulus	mistral	MistralForCausalLM	10000.0	4096	32768

Benchmarks

Original Model Card

UDKai_Garrulus

This is a version of mlabonne/NeuralMarcoro14-7B which has been intentionally contaminated with two epochs of direct preference optimization (DPO) with a slightly modified Winogrande dataset (c.f. winogradov_dpo).

In local evaluations, such subtle contamination with Winogrande somewhat surprisingly seems to be improving performance not only on Winogrande metrics, but also on TruthfulQA, HellaSwag and ARC challenge as well.

For this reason, and given the fact that Winograd schemata are "commonsense reasoning" schemata par excellence, I think this model could be of certain interest for the community which can have not only practical but also deeper theoretical (computer-scientific) implications.

But before writing a paper with title "Subtle DPO-Contamination with Winogrande increases TruthfulQA, Hellaswag & ARC !", let's see what leaderboard evaluation will yield.

🎉 Update

Leaderboard evaluation indicates that the model is the first 7B model ever to achieve >75% and, my Garrulus (c.f. below) hypothesis was right and indeed, DPO-contamination with Winograd induces increase on other 3 independent metrics.

It's weird but it's like that.

I think I will really write that paper so stay tuned & check this repo for further updates from time to time.

DPO adaptation hyperparameters

LoRA:

• r=16
• lora_alpha=16
• lora_dropout=0.05
• bias="none"
• task_type="CAUSAL_LM"
• target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']

Training arguments:

• per_device_train_batch_size=4
• gradient_accumulation_steps=4
• gradient_checkpointing=True
• learning_rate=5e-5
• lr_scheduler_type="cosine"
• max_steps=200
• optim="paged_adamw_32bit"
• warmup_steps=100

DPOTrainer:

• beta=0.1
• max_prompt_length=1024
• max_length=1536

UDK.ai

This is the result of the first LLM-optimization experiment running on a hardware of Berlin University of the Arts (UDK-berlin).

DPO took few minutes on a A40.

Check udk.ai from time to time, we plan to make some noise.

Garrulus

Originally I planned to call the model "ContaminatedWine" but then I had a nice winter encounter with a very convivial eurasian jay (Garrulus Glandarius in latin), hence the name.

Thanks

Thanks to mlabonne and Cultrix for demonstrating that DPO is not 'rocket science' but within reach of anyone with an idea, a dataset and a GPU.

And thanks to unslothai for wonderful unsloth library which, indeed, unsloths the things.