TinyLlama-3T-1.1bee-Q8_0-GGUF / README.md

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	---
	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
	```