jan-hq/Mistral-7B-Instruct-v0.2-DARE

Jan - Discord

Model Description

This model uses the DARE method to merge Mistral-7B-Instruct-v0.2 with 3 leading models in 12th Dec on OpenLLM Leaderboard:

1. OpenHermes-2.5-neural-chat-v3-3-Slerp
2. MetaMath-Cybertron-Starling
3. v1olet_marcoroni-go-bruins-merge-7B

• base model: Mistral-7B-Instruct-v0.2

The yaml config file for this model is here:

base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
  parameters:
    density: 0.8
    weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
  parameters:
    density: 0.8
    weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
  parameters:
    density: 0.8
    weight: 0.3
parameters:
  int8_mask: true

Prompt template:

• ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

• Alpaca

{system_message}

### Instruction:
{prompt}

### Response:

Run this model

You can run this model using Jan Desktop on Mac, Windows, or Linux.

Jan is an open source, ChatGPT alternative that is:

• 💻 100% offline on your machine: Your conversations remain confidential, and visible only to you.
• 🗂️ An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.
• 🌐 OpenAI Compatible: Local server on port 1337 with OpenAI compatible endpoints
• 🌍 Open Source & Free: We build in public; check out our Github

About Jan

Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.

Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.

Jan Model Merger

This is a test project for merging models.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here.

Metric	Value
Avg.	?
ARC (25-shot)	?
HellaSwag (10-shot)	?
MMLU (5-shot)	?
TruthfulQA (0-shot)	?
Winogrande (5-shot)	?
GSM8K (5-shot)	?

Acknowlegement

• mergekit
• DARE
• SLERP
• lm-evaluation-harness

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	55.84
AI2 Reasoning Challenge (25-Shot)	61.95
HellaSwag (10-Shot)	75.62
MMLU (5-Shot)	49.99
TruthfulQA (0-shot)	54.36
Winogrande (5-shot)	74.98
GSM8k (5-shot)	18.12