Quantization made by Richard Erkhov.
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.
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.
This model follows the same prompt format as mistral instruct 7b v0.1 .The sample prompt is still given below:
1
2<s>[INST] Hello, how are you? [/INST]
3
1
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview2")
5model = AutoModelForCausalLM.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview2")
6
7prompt= "<s>[INST] Hello! [/INST] "
8
9device = "cuda"
10
11model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
12model.to(device)
13
14generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
15print(tokenizer.batch_decode(generated_ids)[0])
