TinyMix-8x1b-Chat

This is a MoE-ification of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using the Mixtral branch of mergekit

The Goal was to MoE-fy the TinyLlama model and then use this as a base model to finetune from. The intuition being finetuning 8x1b should give better performance than finetuning 1b by itself.

More work coming!

Chat Template

def make_prompt(instruction):
  return f"<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"

llm.generate(make_prompt('What is quantum tunneling?'))

Mergekit Config

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]

Eval

Thanks to u/mhenrichsen for the HellaSwag score

|  Tasks  |Version|Filter|n-shot| Metric |Value |   |Stderr|

|---------|-------|------|-----:|--------|-----:|---|-----:|

|hellaswag|Yaml   |none  |     0|acc     |0.4657|±  |0.0050|

|         |       |none  |     0|acc\_norm|0.6042|±  |0.0049|