Instructions to use BEE-spoke-data/TinyLlama-3T-1.1bee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/TinyLlama-3T-1.1bee with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BEE-spoke-data/TinyLlama-3T-1.1bee")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/TinyLlama-3T-1.1bee")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/TinyLlama-3T-1.1bee", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BEE-spoke-data/TinyLlama-3T-1.1bee with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/TinyLlama-3T-1.1bee"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/TinyLlama-3T-1.1bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/TinyLlama-3T-1.1bee

SGLang

How to use BEE-spoke-data/TinyLlama-3T-1.1bee with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BEE-spoke-data/TinyLlama-3T-1.1bee" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/TinyLlama-3T-1.1bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BEE-spoke-data/TinyLlama-3T-1.1bee" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/TinyLlama-3T-1.1bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/TinyLlama-3T-1.1bee with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/TinyLlama-3T-1.1bee
```

TinyLlama-3T-1.1bee / README.md

pszemraj

Super-squash branch 'main' using huggingface_hub

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- bees
	- bzz
	- honey
	- oprah winfrey
	datasets:
	- BEE-spoke-data/bees-internal
	metrics:
	- accuracy
	base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
	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
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# TinyLlama-3T-1.1bee


	![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/I6AfPId0Xo_vVobtkAP12.png)

	A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements:

	- start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
	- vastly improved and expanded SoTA beekeeping dataset


	## Model description

	This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset.

	It achieves the following results on the evaluation set:
	- Loss: 2.1640
	- Accuracy: 0.5406

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0001
	- train_batch_size: 4
	- eval_batch_size: 2
	- seed: 13707
	- gradient_accumulation_steps: 16
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.05
	- num_epochs: 2.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 2.4432 \| 0.19 \| 50 \| 2.3850 \| 0.5033 \|
	\| 2.3655 \| 0.39 \| 100 \| 2.3124 \| 0.5129 \|
	\| 2.374 \| 0.58 \| 150 \| 2.2588 \| 0.5215 \|
	\| 2.3558 \| 0.78 \| 200 \| 2.2132 \| 0.5291 \|
	\| 2.2677 \| 0.97 \| 250 \| 2.1828 \| 0.5348 \|
	\| 2.0701 \| 1.17 \| 300 \| 2.1788 \| 0.5373 \|
	\| 2.0766 \| 1.36 \| 350 \| 2.1673 \| 0.5398 \|
	\| 2.0669 \| 1.56 \| 400 \| 2.1651 \| 0.5402 \|
	\| 2.0314 \| 1.75 \| 450 \| 2.1641 \| 0.5406 \|
	\| 2.0281 \| 1.95 \| 500 \| 2.1639 \| 0.5407 \|


	### Framework versions

	- Transformers 4.36.2
	- Pytorch 2.1.0
	- Datasets 2.16.1
	- Tokenizers 0.15.0
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__TinyLlama-3T-1.1bee)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|36.46\|
	\|AI2 Reasoning Challenge (25-Shot)\|33.79\|
	\|HellaSwag (10-Shot) \|60.29\|
	\|MMLU (5-Shot) \|25.86\|
	\|TruthfulQA (0-shot) \|38.13\|
	\|Winogrande (5-shot) \|60.22\|
	\|GSM8k (5-shot) \| 0.45\|