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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TinyLlama-3T-1.1bee

A grand successor to the original. This one has the following improvements:

start from finished 3T TinyLlama
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

Detailed results can be found here

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