Instructions to use veyra-ai/veyra2-30m-instruct-early-onnx-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use veyra-ai/veyra2-30m-instruct-early-onnx-int8 with Transformers:

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

pipe = pipeline("text-generation", model="veyra-ai/veyra2-30m-instruct-early-onnx-int8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("veyra-ai/veyra2-30m-instruct-early-onnx-int8")
model = AutoModelForCausalLM.from_pretrained("veyra-ai/veyra2-30m-instruct-early-onnx-int8")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use veyra-ai/veyra2-30m-instruct-early-onnx-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "veyra-ai/veyra2-30m-instruct-early-onnx-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "veyra-ai/veyra2-30m-instruct-early-onnx-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/veyra-ai/veyra2-30m-instruct-early-onnx-int8

SGLang

How to use veyra-ai/veyra2-30m-instruct-early-onnx-int8 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 "veyra-ai/veyra2-30m-instruct-early-onnx-int8" \
    --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": "veyra-ai/veyra2-30m-instruct-early-onnx-int8",
		"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 "veyra-ai/veyra2-30m-instruct-early-onnx-int8" \
        --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": "veyra-ai/veyra2-30m-instruct-early-onnx-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use veyra-ai/veyra2-30m-instruct-early-onnx-int8 with Docker Model Runner:
```
docker model run hf.co/veyra-ai/veyra2-30m-instruct-early-onnx-int8
```

veyra2-30m-instruct-v0.1-onnx-int8

ONNX Runtime dynamic INT8 export of a Veyra Stage 3 checkpoint.

Notes

This repo contains quantized ONNX files exported with optimum-cli export onnx using:

optimum-cli export onnx --task text-generation-with-past --opset 17
Then the ONNX files were dynamically quantized to INT8 with ONNX Runtime.

The original tokenizer and config files are included.

Downloads last month: 4

Safetensors

Model size

34.6M params

Tensor type

BF16

Collection including veyra-ai/veyra2-30m-instruct-early-onnx-int8

Veyra2

Collection

The second generation of Veyra, these models are meant for local CPU inference. • 6 items • Updated 10 days ago