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
model-index:
  - name: KnowledgeNinja-LiteLlama-460Mx6MoE-1T
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 25.17
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 38.45
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 26.16
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 41.57
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 50.04
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T
          name: Open LLM Leaderboard

KnowledgeNinja-LiteLlama-460Mx6MoE-1T

KnowledgeNinja-LiteLlama-460Mx6MoE-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: ["Accounting"]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: ["Finance"]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: ["Strategy"]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: ["Marketing"]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: ["Organizational Behaviour"]
  - source_model: ahxt/LiteLlama-460M-1T
    positive_prompts: ["Economics"]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.23
AI2 Reasoning Challenge (25-Shot)	25.17
HellaSwag (10-Shot)	38.45
MMLU (5-Shot)	26.16
TruthfulQA (0-shot)	41.57
Winogrande (5-shot)	50.04
GSM8k (5-shot)	0.00