synapsellm-7b-mistral-v0.4-preview

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: 32
• 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 770k rows comprising of 361k Maths Instruct Q/A, 143k GPT-3.5 Q/A, 140k General Code, 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

Prompt format:

This model follows the same prompt format as mistral instruct 7b v0.1 .The sample prompt is still given below:

<s>[INST] Hello, how are you? [/INST]

Example Code:

Here's an example code using transformers library provided by HF.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview")
model = AutoModelForCausalLM.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview")

prompt= "<s>[INST] Hello!  [/INST] "

device = "cuda"

model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
model.to(device)

generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
print(tokenizer.batch_decode(generated_ids)[0])

Model Bias:

This model has some bias areas, discussed below:

• Model might output factually incorrect information.
• Model does not follow system prompts.
• Model does not have any kind of memory, researchers can experiment feeding memory.
• Model is trained on different datas, so it can bias information or exclaim itself as gpt model.
• Model might return "Ground Truth: " in the response and duplicate responses.