SynapseLLM:
SynapseLLM, a significant achievement by WebraftAI, represents a series of large language AI models designed to create robust, generalized, and decentralized information systems. This repository specifically houses the SynapseLLM finetuned version of Mistral. The finetuning process is conducted on a custom dataset, albeit limited in scope, focusing on code and normal question-answering scenarios. This adaptation showcases the model's versatility and applicability within specific domains, contributing to the broader landscape of AI advancements.
Model Details
SynapseLLM:
- Parameters: 7B
- Learning rate: 2e-4
- Adapter used: Qlora
- Precision: float16
- Batch size: 16
- Maximum gradient normal: 0.3
- Optimizer: paged_adamw_32bit
- Warmup Ratio: 0.03
- Step(s) (trained): 100
- Epoch(s) (trained): 1
Model Description
This is a 7b parameter, decoder only transformer based finetuned model on Chat Q/A and Code instructions. It's a preview finetune on Mistral 7B v0.1 on a sample dataset of 409k rows comprising of 140k General Code, 143k GPT-3.5 Q/A, 63k Python code, and 54k General Q/A (Through GPT-4) [Each row contains one instruction and one response]. This is a full model merged and compiled with trained adapters, so you can easily load this through transformers library.
- Developed by: WebraftAI
- Funded by: Webraft Cloud
- Shared by: WebraftAI
- Model type: Decoder-only Transformer
- Language(s): English Only
- License: Apache 2.0
- Finetuned from model: Mistral-7b-v0.1
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 57.01 |
| AI2 Reasoning Challenge (25-Shot) | 53.84 |
| HellaSwag (10-Shot) | 74.86 |
| MMLU (5-Shot) | 54.81 |
| TruthfulQA (0-shot) | 55.03 |
| Winogrande (5-shot) | 74.59 |
| GSM8k (5-shot) | 28.96 |