Memgpt-3x7b-MOE
Memgpt-3x7b-MOE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
base_model: liminerity/Memgpt-slerp-7b-5
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: starsnatched/MemGPT-DPO
positive_prompts:
- "versatile"
- "helpful"
- "factual"
- "integrated"
- "adaptive"
- "comprehensive"
- "balanced"
negative_prompts:
- "specialized"
- "narrow"
- "focused"
- "limited"
- "specific"
- source_model: starsnatched/MemGPT-3
positive_prompts:
- "analytical"
- "accurate"
- "logical"
- "knowledgeable"
- "precise"
- "calculate"
- "compute"
- "solve"
- "work"
- "python"
- "javascript"
- "programming"
- "algorithm"
- "tell me"
- "assistant"
negative_prompts:
- "creative"
- "abstract"
- "imaginative"
- "artistic"
- "emotional"
- "mistake"
- "inaccurate"
- source_model: starsnatched/MemGPT
positive_prompts:
- "instructive"
- "clear"
- "directive"
- "helpful"
- "informative"
negative_prompts:
- "exploratory"
- "open-ended"
- "narrative"
- "speculative"
- "artistic"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/Memgpt-3x7b-MOE"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])