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

Libraries

How to use BEE-spoke-data/TinyLlama-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-1.1bee")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/TinyLlama-1.1bee")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/TinyLlama-1.1bee")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BEE-spoke-data/TinyLlama-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-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-1.1bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use BEE-spoke-data/TinyLlama-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-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-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-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-1.1bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/TinyLlama-1.1bee with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/TinyLlama-1.1bee
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TinyLlama-1.1bee 🐝

As we feverishly hit the refresh button on hf.co's homepage, on the hunt for the newest waifu chatbot to grace the AI stage, an epiphany struck us like a bee sting. What could we offer to the hive-mind of the community? The answer was as clear as honey—beekeeping, naturally. And thus, this un-bee-lievable model was born.

Details

This model is a fine-tuned version of PY007/TinyLlama-1.1B-intermediate-step-240k-503b on the BEE-spoke-data/bees-internal dataset. It achieves the following results on the evaluation set:

Loss: 2.4285
Accuracy: 0.4969

***** eval metrics *****
  eval_accuracy           =     0.4972                                              
  eval_loss               =     2.4283
  eval_runtime            = 0:00:53.12
  eval_samples            =        239
  eval_samples_per_second =      4.499
  eval_steps_per_second   =      1.129
  perplexity              =    11.3391

📜 Intended Uses & Limitations 📜

Intended Uses:

Educational Engagement: Whether you're a novice beekeeper, an enthusiast, or someone just looking to understand the buzz around bees, this model aims to serve as an informative and entertaining resource.
General Queries: Have questions about hive management, bee species, or honey extraction? Feel free to consult the model for general insights.
Academic & Research Inspiration: If you're diving into the world of apiculture studies or environmental science, our model could offer some preliminary insights and ideas.

Limitations:

Not a Beekeeping Expert: As much as we admire bees and their hard work, this model is not a certified apiculturist. Please consult professional beekeeping resources or experts for serious decisions related to hive management, bee health, and honey production.
Licensing: Apache-2.0, following TinyLlama
Infallibility: Our model can err, just like any other piece of technology (or bee). Always double-check the information before applying it to your own hive or research.
Ethical Constraints: This model may not be used for any illegal or unethical activities, including but not limited to: bioterrorism & standard terrorism, harassment, or spreading disinformation.

Training and evaluation data

While the full dataset is not yet complete and therefore not yet released for "safety reasons", you can check out a preliminary sample at: bees-v0

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 80085
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.15
ARC (25-shot)	30.55
HellaSwag (10-shot)	51.8
MMLU (5-shot)	24.25
TruthfulQA (0-shot)	39.01
Winogrande (5-shot)	54.46
GSM8K (5-shot)	0.23
DROP (3-shot)	3.74