---
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`](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee) for more details on the model.
## Use with llama.cpp

Install llama.cpp through brew.

```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.

CLI:

```bash
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:

```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```