Instructions to use hypaai/hypa-tiny-keys with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hypaai/hypa-tiny-keys with Transformers:

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

pipe = pipeline("text-generation", model="hypaai/hypa-tiny-keys")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hypaai/hypa-tiny-keys")
model = AutoModelForCausalLM.from_pretrained("hypaai/hypa-tiny-keys", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hypaai/hypa-tiny-keys with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hypaai/hypa-tiny-keys"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hypaai/hypa-tiny-keys

SGLang

How to use hypaai/hypa-tiny-keys 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 "hypaai/hypa-tiny-keys" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "hypaai/hypa-tiny-keys" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hypaai/hypa-tiny-keys",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hypaai/hypa-tiny-keys with Docker Model Runner:
```
docker model run hf.co/hypaai/hypa-tiny-keys
```

hypa-tiny-keys / README.md

hypaai

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	---
	library_name: transformers
	tags:
	- generated_from_trainer
	model-index:
	- name: hypa-tiny-keys
	results: []
	---

	<!-- 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. -->

	# hypa-tiny-keys

	This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1994

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0003
	- train_batch_size: 256
	- eval_batch_size: 256
	- seed: 3407
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- training_steps: 183476

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:------:\|:---------------:\|
	\| 2.0830 \| 0.0250 \| 4587 \| 1.5913 \|
	\| 1.4610 \| 0.0500 \| 9174 \| 1.5099 \|
	\| 1.3978 \| 0.0750 \| 13761 \| 1.4691 \|
	\| 1.3647 \| 0.1000 \| 18348 \| 1.4470 \|
	\| 1.3525 \| 0.1250 \| 22935 \| 1.4381 \|
	\| 1.3103 \| 0.1500 \| 27522 \| 1.4193 \|
	\| 1.3251 \| 0.1750 \| 32109 \| 1.4158 \|
	\| 1.2983 \| 0.2000 \| 36696 \| 1.4092 \|
	\| 1.2916 \| 0.2250 \| 41283 \| 1.4117 \|
	\| 1.2891 \| 0.2500 \| 45870 \| 1.3917 \|
	\| 1.2785 \| 0.2750 \| 50457 \| 1.3796 \|
	\| 1.2719 \| 0.3000 \| 55044 \| 1.3855 \|
	\| 1.2648 \| 0.3250 \| 59631 \| 1.3817 \|
	\| 1.2662 \| 0.3500 \| 64218 \| 1.3654 \|
	\| 1.2446 \| 0.3750 \| 68805 \| 1.3616 \|
	\| 1.2584 \| 0.4000 \| 73392 \| 1.3651 \|
	\| 1.2331 \| 0.4250 \| 77979 \| 1.3469 \|
	\| 1.2437 \| 0.4500 \| 82566 \| 1.3525 \|
	\| 1.2234 \| 0.4750 \| 87153 \| 1.3285 \|
	\| 1.2318 \| 0.5000 \| 91740 \| 1.3416 \|
	\| 1.2186 \| 0.5250 \| 96327 \| 1.3188 \|
	\| 1.2173 \| 0.5500 \| 100914 \| 1.3146 \|
	\| 1.2145 \| 0.5750 \| 105501 \| 1.3058 \|
	\| 1.1944 \| 0.6000 \| 110088 \| 1.3002 \|
	\| 1.2025 \| 0.6250 \| 114675 \| 1.2898 \|
	\| 1.1773 \| 0.6500 \| 119262 \| 1.2806 \|
	\| 1.1972 \| 0.6750 \| 123849 \| 1.2718 \|
	\| 1.1623 \| 0.7000 \| 128436 \| 1.2427 \|
	\| 1.1875 \| 0.7250 \| 133023 \| 1.2537 \|
	\| 1.1675 \| 0.7500 \| 137610 \| 1.2442 \|
	\| 1.1576 \| 0.7750 \| 142197 \| 1.2418 \|
	\| 1.1725 \| 0.8000 \| 146784 \| 1.2313 \|
	\| 1.1483 \| 0.8250 \| 151371 \| 1.2312 \|
	\| 1.1634 \| 0.8500 \| 155958 \| 1.2177 \|
	\| 1.1455 \| 0.8750 \| 160545 \| 1.2165 \|
	\| 1.1637 \| 0.9000 \| 165132 \| 1.2105 \|
	\| 1.1420 \| 0.9250 \| 169719 \| 1.2045 \|
	\| 1.1645 \| 0.9500 \| 174306 \| 1.2024 \|
	\| 1.1522 \| 0.9750 \| 178893 \| 1.2000 \|
	\| 1.1522 \| 1.0 \| 183476 \| 1.1994 \|


	### Framework versions

	- Transformers 5.17.0
	- Pytorch 2.8.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.23.2

	---
	library_name: transformers
	tags:
	- generated_from_trainer
	model-index:
	- name: hypa-tiny-keys
	results: []
	---

	<!-- 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. -->

	# hypa-tiny-keys

	This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1994

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0003
	- train_batch_size: 256
	- eval_batch_size: 256
	- seed: 3407
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- training_steps: 183476

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:------:\|:---------------:\|
	\| 2.0830 \| 0.0250 \| 4587 \| 1.5913 \|
	\| 1.4610 \| 0.0500 \| 9174 \| 1.5099 \|
	\| 1.3978 \| 0.0750 \| 13761 \| 1.4691 \|
	\| 1.3647 \| 0.1000 \| 18348 \| 1.4470 \|
	\| 1.3525 \| 0.1250 \| 22935 \| 1.4381 \|
	\| 1.3103 \| 0.1500 \| 27522 \| 1.4193 \|
	\| 1.3251 \| 0.1750 \| 32109 \| 1.4158 \|
	\| 1.2983 \| 0.2000 \| 36696 \| 1.4092 \|
	\| 1.2916 \| 0.2250 \| 41283 \| 1.4117 \|
	\| 1.2891 \| 0.2500 \| 45870 \| 1.3917 \|
	\| 1.2785 \| 0.2750 \| 50457 \| 1.3796 \|
	\| 1.2719 \| 0.3000 \| 55044 \| 1.3855 \|
	\| 1.2648 \| 0.3250 \| 59631 \| 1.3817 \|
	\| 1.2662 \| 0.3500 \| 64218 \| 1.3654 \|
	\| 1.2446 \| 0.3750 \| 68805 \| 1.3616 \|
	\| 1.2584 \| 0.4000 \| 73392 \| 1.3651 \|
	\| 1.2331 \| 0.4250 \| 77979 \| 1.3469 \|
	\| 1.2437 \| 0.4500 \| 82566 \| 1.3525 \|
	\| 1.2234 \| 0.4750 \| 87153 \| 1.3285 \|
	\| 1.2318 \| 0.5000 \| 91740 \| 1.3416 \|
	\| 1.2186 \| 0.5250 \| 96327 \| 1.3188 \|
	\| 1.2173 \| 0.5500 \| 100914 \| 1.3146 \|
	\| 1.2145 \| 0.5750 \| 105501 \| 1.3058 \|
	\| 1.1944 \| 0.6000 \| 110088 \| 1.3002 \|
	\| 1.2025 \| 0.6250 \| 114675 \| 1.2898 \|
	\| 1.1773 \| 0.6500 \| 119262 \| 1.2806 \|
	\| 1.1972 \| 0.6750 \| 123849 \| 1.2718 \|
	\| 1.1623 \| 0.7000 \| 128436 \| 1.2427 \|
	\| 1.1875 \| 0.7250 \| 133023 \| 1.2537 \|
	\| 1.1675 \| 0.7500 \| 137610 \| 1.2442 \|
	\| 1.1576 \| 0.7750 \| 142197 \| 1.2418 \|
	\| 1.1725 \| 0.8000 \| 146784 \| 1.2313 \|
	\| 1.1483 \| 0.8250 \| 151371 \| 1.2312 \|
	\| 1.1634 \| 0.8500 \| 155958 \| 1.2177 \|
	\| 1.1455 \| 0.8750 \| 160545 \| 1.2165 \|
	\| 1.1637 \| 0.9000 \| 165132 \| 1.2105 \|
	\| 1.1420 \| 0.9250 \| 169719 \| 1.2045 \|
	\| 1.1645 \| 0.9500 \| 174306 \| 1.2024 \|
	\| 1.1522 \| 0.9750 \| 178893 \| 1.2000 \|
	\| 1.1522 \| 1.0 \| 183476 \| 1.1994 \|


	### Framework versions

	- Transformers 5.17.0
	- Pytorch 2.8.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.23.2