TinyMix-8x1b

This is a MoE-ification of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T using the Mixtral branch of mergekit

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

More work coming!

Inference Template

This is a merge of the base model, so treat it like a completion.

llm.generate('Quantum Tunneling is')

Mergekit Config

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]

Eval

Thanks to u/mhenrichsen for thr HellaSwag score

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

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

|hellaswag|Yaml   |none  |     0|acc     |0.4659|±  |0.0050|

|         |       |none  |     0|acc\_norm|0.6044|±  |0.0049|