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RichardErkhov/
argilla_-_distilabeled-Marcoro14-7B-slerp-full-gguf

Name: argilla_-_distilabeled-Marcoro14-7B-slerp-full-gguf
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distilabeled-Marcoro14-7B-slerp-full - GGUF

Model creator: https://huggingface.co/argilla/
Original model: https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp-full/

Name	Quant method	Size
distilabeled-Marcoro14-7B-slerp-full.Q2_K.gguf	Q2_K	2.53GB
distilabeled-Marcoro14-7B-slerp-full.IQ3_XS.gguf	IQ3_XS	2.81GB
distilabeled-Marcoro14-7B-slerp-full.IQ3_S.gguf	IQ3_S	2.96GB
distilabeled-Marcoro14-7B-slerp-full.Q3_K_S.gguf	Q3_K_S	2.95GB
distilabeled-Marcoro14-7B-slerp-full.IQ3_M.gguf	IQ3_M	3.06GB
distilabeled-Marcoro14-7B-slerp-full.Q3_K.gguf	Q3_K	3.28GB
distilabeled-Marcoro14-7B-slerp-full.Q3_K_M.gguf	Q3_K_M	3.28GB
distilabeled-Marcoro14-7B-slerp-full.Q3_K_L.gguf	Q3_K_L	3.56GB
distilabeled-Marcoro14-7B-slerp-full.IQ4_XS.gguf	IQ4_XS	3.67GB
distilabeled-Marcoro14-7B-slerp-full.Q4_0.gguf	Q4_0	3.83GB
distilabeled-Marcoro14-7B-slerp-full.IQ4_NL.gguf	IQ4_NL	3.87GB
distilabeled-Marcoro14-7B-slerp-full.Q4_K_S.gguf	Q4_K_S	3.86GB
distilabeled-Marcoro14-7B-slerp-full.Q4_K.gguf	Q4_K	4.07GB
distilabeled-Marcoro14-7B-slerp-full.Q4_K_M.gguf	Q4_K_M	4.07GB
distilabeled-Marcoro14-7B-slerp-full.Q4_1.gguf	Q4_1	4.24GB
distilabeled-Marcoro14-7B-slerp-full.Q5_0.gguf	Q5_0	4.65GB
distilabeled-Marcoro14-7B-slerp-full.Q5_K_S.gguf	Q5_K_S	4.65GB
distilabeled-Marcoro14-7B-slerp-full.Q5_K.gguf	Q5_K	4.78GB
distilabeled-Marcoro14-7B-slerp-full.Q5_K_M.gguf	Q5_K_M	4.78GB
distilabeled-Marcoro14-7B-slerp-full.Q5_1.gguf	Q5_1	5.07GB
distilabeled-Marcoro14-7B-slerp-full.Q6_K.gguf	Q6_K	5.53GB
distilabeled-Marcoro14-7B-slerp-full.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language:

en license: apache-2.0 tags:
distilabel
dpo
rlaif
rlhf
merge
mergekit datasets:
argilla/distilabel-intel-orca-dpo-pairs model-index:
name: distilabeled-Marcoro14-7B-slerp-full results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 70.65 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 87.55 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 65.33 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 64.21 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 82.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 70.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full name: Open LLM Leaderboard

⚗️ distilabeled Marcoro14 7B Slerp

Introduction

This model is a new DPO fine-tune of our new open dataset argilla/distilabel-intel-orca-dpo-pairs, on the mlabonne/Marcoro14-7B-slerp model. You can find more information of the "distilabeled" dataset used at this repo argilla/distilabeled-Hermes-2.5-Mistral-7B, and visit distilabel.

The difference between this model and argilla/distilabeled-Marcoro14-7B-slerp is that this model has been fine-tuned for a whole epoch instead instead of 200 steps, so it has seen the whole dataset.

Training details

As we did with Notus, we wanted a reproducible recipe to test the impact of data quality.

And we're lucky to have so many amazing folks in the open community contributing reproducible, easy-to-use training scripts and recipes. This time, Maxime Labonne had shared a Colab to fine-tune OpenHermes with DPO and the original Intel's dataset, perfect! We just updated the base model to mlabonne/Marcoro14-7B-slerp, and applied the same dataset recipe we used for argilla/distilabeled-Hermes-2.5-Mistral-7B:

python

from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# we did this
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r: 
        r["status"] != "tie" and 
        r["chosen_score"] >= 8 and 
        not r["in_gsm8k_train"]
)

Benchmark results

For benchmarking we used the famous "Nous" or "Teknium" benchmark. You can find below an overview, including our first experiment with a less ambitious dataset filtering (removing ties and score>5).

For running the benchmark we used another awesome contribution from Maxime: LLM AutoEval, check it out!

Model	AGIEval	GPT4ALL	TruthfulQA	Bigbench	Average
argilla/distilabeled-Marcoro14-7B-slerp-full	45.17	76.59	64.68	48.15	58.65
argilla/distilabeled-Marcoro14-7B-slerp	45.4	76.47	65.46	47.19	58.63
Marcoro14-7B-slerp	44.66	76.24	64.15	45.64	57.67
argilla/distilabeled-Hermes-2.5-Mistral-7B	44.64	73.35	55.96	42.21	54.04

Training Hardware

We used 1 x A100 80GB in runpod for less than 2 hours.

Acknowledgements

We'd like to thank the amazing open community and in particular:

The Intel team for publishing a great open dataset and show how well it worked in the first place
Teknium and NousResearch for their awesome work and models.
Maxime for sharing such great resources.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.40
AI2 Reasoning Challenge (25-Shot)	70.65
HellaSwag (10-Shot)	87.55
MMLU (5-Shot)	65.33
TruthfulQA (0-shot)	64.21
Winogrande (5-shot)	82.00
GSM8k (5-shot)	70.66

RichardErkhov/argilla_-_distilabeled-Marcoro14-7B-slerp-full-gguf