Instructions to use JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain with Transformers:

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

pipe = pipeline("text-generation", model="JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain")
model = AutoModelForCausalLM.from_pretrained("JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain

SGLang

How to use JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain 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 "JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain" \
    --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": "JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain",
		"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 "JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain" \
        --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": "JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain with Docker Model Runner:
```
docker model run hf.co/JuliaKreutzerCohere/tiny-aya-global-prompt-postexplain
```

JuliaKreutzerCohere commited on Jul 13

Commit

326bc8a

verified ·

1 Parent(s): 9d90525

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

.gitattributes +15 -0
wheels/regex-2026.7.10-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl +3 -0

.gitattributes CHANGED Viewed

@@ -35,3 +35,18 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 assets/TinyAya_Global.png filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 assets/TinyAya_Global.png filter=lfs diff=lfs merge=lfs -text
+wheels/certifi-2026.6.17-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/charset_normalizer-3.4.9-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/fsspec-2026.6.0-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/hf_xet-1.5.1-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/huggingface_hub-0.36.2-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/packaging-26.2-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/pyyaml-6.0.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/regex-2026.6.28-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/regex-2026.7.10-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/safetensors-0.8.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl filter=lfs diff=lfs merge=lfs -text
+wheels/tqdm-4.68.4-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/transformers-4.56.2-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+wheels/urllib3-2.7.0-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text

wheels/regex-2026.7.10-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:53bbbd6c610489700f7110db1d85f3623924c3f7c760f987eca033867360788a
+size 794164