Instructions to use RekaAI/reka-edge-2603 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RekaAI/reka-edge-2603 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="RekaAI/reka-edge-2603", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("RekaAI/reka-edge-2603", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RekaAI/reka-edge-2603 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RekaAI/reka-edge-2603" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RekaAI/reka-edge-2603", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/RekaAI/reka-edge-2603
- SGLang
How to use RekaAI/reka-edge-2603 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 "RekaAI/reka-edge-2603" \ --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": "RekaAI/reka-edge-2603", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "RekaAI/reka-edge-2603" \ --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": "RekaAI/reka-edge-2603", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use RekaAI/reka-edge-2603 with Docker Model Runner:
docker model run hf.co/RekaAI/reka-edge-2603
| set -euo pipefail | |
| # Quantize text decoder GGUF with: | |
| # - default Q4_0 | |
| # - last 8 transformer blocks (24..31) overridden to Q8_0 | |
| # | |
| # Usage: ./scripts/quantize_reka_q4_last8_q8.sh [INPUT_GGUF] [OUTPUT_GGUF] | |
| # Optional: QUANTIZE_BIN (default: build/bin or build_linux/bin/llama-quantize) | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" | |
| cd "$REPO_ROOT" | |
| INPUT_GGUF="${1:-}" | |
| OUTPUT_GGUF="${2:-}" | |
| THREADS="${THREADS:-16}" | |
| if [[ -z "$INPUT_GGUF" || -z "$OUTPUT_GGUF" ]]; then | |
| echo "Usage: $0 <INPUT_F16_GGUF> <OUTPUT_GGUF>" >&2 | |
| exit 1 | |
| fi | |
| if [[ -z "${QUANTIZE_BIN:-}" ]]; then | |
| if [[ -x "$REPO_ROOT/build_linux/bin/llama-quantize" ]]; then | |
| QUANTIZE_BIN="$REPO_ROOT/build_linux/bin/llama-quantize" | |
| else | |
| QUANTIZE_BIN="$REPO_ROOT/build/bin/llama-quantize" | |
| fi | |
| fi | |
| "$QUANTIZE_BIN" \ | |
| --tensor-type 'blk\.(2[4-9]|3[01])\..*=Q8_0' \ | |
| "$INPUT_GGUF" \ | |
| "$OUTPUT_GGUF" \ | |
| Q4_0 \ | |
| "$THREADS" | |