license: cc-by-nc-4.0
tags:
- merge
- conversational
- multi-task
pipeline_tag: text-generation
base_model:
- paulml/OmniBeagleSquaredMBX-v3-7B
- ZySec-AI/ZySec-7B-v1
- liminerity/Omningotex-7b-slerp
- localfultonextractor/Erosumika-7B
- KatyTheCutie/LemonadeRP-4.5.3
- cgato/Thespis-Krangled-7b
- CorticalStack/pastiche-crown-clown-7b-dare
- snorkelai/Snorkel-Mistral-PairRM-DPO
- MTSAIR/multi_verse_model
model-index:
- name: winter-garden-7b-alpha - "Smart Assistant"
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: 65.19
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
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: 85.36
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
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: 65.2
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
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: 50.94
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
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: 80.35
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
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: 54.44
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha
name: Open LLM Leaderboard
Winter Garden 7B - α - "Smart Assistant"
It was mentioned that we are in the open ai dark winter; so I thought I would make myself a nice winter garden.
An experiment
I've merged four partitions successfully in the past, so lets go for 9! I started with:
- Mistral-7B-v0.1
and merged in
- OmniBeagleSquaredMBX-v3-7B
- ZySec-7B-v1
- Omningotex-7b-slerp
- Erosumika-7B
- LemonadeRP-4.5.3
- Thespis-Krangled-7b
- pastiche-crown-clown-7b-dare
- Snorkel-Mistral-PairRM-DPO
- multi_verse_model
9-partition merge
All of the layers were partitioned in to 9 random bins. Alternating models were slerped at [0...1], and [1...0] gradients; except attention, which was slerped at 0.03.
This means that the model is still predominantly ordered around base mistral - including half of the input and output layers, and 28% of attention.
Other
Includes fast tokenizer.
Chat Template
I put a conversational chat template, which takes "name", "to" (optional), and "content" as the turns. It is designed to follow a transcript style chat which is used by some of the models. This type of use-case is best done by outlining a scene and creating a character card.
### {% title %}
{% metadata %}
USER: Hello
ASSISTANT: Hi, how are you?
It leans to being a coder when given an ### Instruction, follows <s>[INST][/INST], and likes <|user|>, <|assistant|> as well.
A quite cheery and intelligent model. Very good with science and math, but still capable of a decent amount of creativity for a 7b model.
Scores
| Metric | Score |
|---|---|
| Average | 66.91 |
| ARC | 65.19 |
| HellaSwag | 85.36 |
| MMLU | 65.2 |
| TruthfulQA | 50.94 |
| Winogrande | 80.35 |
| GSM8K | 54.44 |