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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- bees
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- bzz
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- honey
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- oprah winfrey
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- llama-cpp
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- gguf-my-repo
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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datasets:
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- BEE-spoke-data/bees-internal
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metrics:
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- accuracy
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inference:
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parameters:
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max_new_tokens: 64
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do_sample: true
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renormalize_logits: true
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repetition_penalty: 1.05
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no_repeat_ngram_size: 6
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temperature: 0.9
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top_p: 0.95
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epsilon_cutoff: 0.0008
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widget:
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- text: In beekeeping, the term "queen excluder" refers to
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example_title: Queen Excluder
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- text: One way to encourage a honey bee colony to produce more honey is by
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example_title: Increasing Honey Production
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- text: The lifecycle of a worker bee consists of several stages, starting with
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example_title: Lifecycle of a Worker Bee
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- text: Varroa destructor is a type of mite that
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example_title: Varroa Destructor
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- text: In the world of beekeeping, the acronym PPE stands for
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example_title: Beekeeping PPE
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- text: The term "robbing" in beekeeping refers to the act of
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example_title: Robbing in Beekeeping
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- text: 'Question: What''s the primary function of drone bees in a hive?
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Answer:'
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example_title: Role of Drone Bees
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- text: To harvest honey from a hive, beekeepers often use a device known as a
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example_title: Honey Harvesting Device
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- text: 'Problem: You have a hive that produces 60 pounds of honey per year. You decide
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to split the hive into two. Assuming each hive now produces at a 70% rate compared
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to before, how much honey will you get from both hives next year?
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To calculate'
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example_title: Beekeeping Math Problem
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- text: In beekeeping, "swarming" is the process where
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example_title: Swarming
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pipeline_tag: text-generation
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model-index:
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- name: TinyLlama-3T-1.1bee
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 33.79
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 60.29
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 25.86
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 38.13
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 60.22
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 0.45
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
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name: Open LLM Leaderboard
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---
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# DavidAU/TinyLlama-3T-1.1bee-Q8_0-GGUF
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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.
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Refer to the [original model card](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew.
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```bash
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brew install ggerganov/ggerganov/llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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CLI:
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```bash
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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"
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```
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Server:
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```bash
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llama-server --hf-repo DavidAU/TinyLlama-3T-1.1bee-Q8_0-GGUF --model tinyllama-3t-1.1bee.Q8_0.gguf -c 2048
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```
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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.
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```
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git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-3t-1.1bee.Q8_0.gguf -n 128
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```
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