README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - bees
  - bzz
  - honey
  - oprah winfrey
  - llama-cpp
  - gguf-my-repo
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
datasets:
  - BEE-spoke-data/bees-internal
metrics:
  - accuracy
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    renormalize_logits: true
    repetition_penalty: 1.05
    no_repeat_ngram_size: 6
    temperature: 0.9
    top_p: 0.95
    epsilon_cutoff: 0.0008
widget:
  - text: In beekeeping, the term "queen excluder" refers to
    example_title: Queen Excluder
  - text: One way to encourage a honey bee colony to produce more honey is by
    example_title: Increasing Honey Production
  - text: The lifecycle of a worker bee consists of several stages, starting with
    example_title: Lifecycle of a Worker Bee
  - text: Varroa destructor is a type of mite that
    example_title: Varroa Destructor
  - text: In the world of beekeeping, the acronym PPE stands for
    example_title: Beekeeping PPE
  - text: The term "robbing" in beekeeping refers to the act of
    example_title: Robbing in Beekeeping
  - text: |-
      Question: What's the primary function of drone bees in a hive?
      Answer:
    example_title: Role of Drone Bees
  - text: To harvest honey from a hive, beekeepers often use a device known as a
    example_title: Honey Harvesting Device
  - text: >-
      Problem: You have a hive that produces 60 pounds of honey per year. You
      decide to split the hive into two. Assuming each hive now produces at a
      70% rate compared to before, how much honey will you get from both hives
      next year?

      To calculate
    example_title: Beekeeping Math Problem
  - text: In beekeeping, "swarming" is the process where
    example_title: Swarming
pipeline_tag: text-generation
model-index:
  - name: TinyLlama-3T-1.1bee
    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: 33.79
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          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: 60.29
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          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: 25.86
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          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: 38.13
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          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: 60.22
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          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: 0.45
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
          name: Open LLM Leaderboard

DavidAU/TinyLlama-3T-1.1bee-Q8_0-GGUF

This model was converted to GGUF format from BEE-spoke-data/TinyLlama-3T-1.1bee using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo DavidAU/TinyLlama-3T-1.1bee-Q8_0-GGUF --model tinyllama-3t-1.1bee.Q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo DavidAU/TinyLlama-3T-1.1bee-Q8_0-GGUF --model tinyllama-3t-1.1bee.Q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m tinyllama-3t-1.1bee.Q8_0.gguf -n 128