Proximus-2x7B-v1 is a Mixure of Experts (MoE) made with the following models using
LazyMergekit:
1base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
2experts:
3 - source_model: beowolx/MistralHermes-CodePro-7B-v1
4 positive_prompts:
5 - "code"
6 - "python"
7 - "javascript"
8 - "programming"
9 - "algorithm"
10 - source_model: preemware/Prox-MistralHermes-7B
11 positive_prompts:
12 - "cybersecurity"
13 - "information security"
14 - "network security"
15 - "hacking"
16 - "encryption"
1!pip install -qU transformers bitsandbytes accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "preemware/Proximus-2x7B-v1"
8
9tokenizer = AutoTokenizer.from_pretrained(model)
10pipeline = transformers.pipeline(
11 "text-generation",
12 model=model,
13 model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
14)
15
16messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
17prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
18outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
19print(outputs[0]["generated_text"])
