metadata

license: apache-2.0
tags:
  - moe
  - merge
  - mergekit
  - lazymergekit
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T
  - ahxt/LiteLlama-460M-1T

LiteLlamix-8x460M-1T

LiteLlamix-8x460M-1T is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

base_model: ahxt/LiteLlama-460M-1T
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: [""]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "aloobun/LiteLlamix-8x460M-1T"

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"])