Image-Text-to-Text
Transformers
Safetensors
deepseek_v41
text-generation
conversational
8-bit precision
fp8
Instructions to use maletsidibeOS/Anos_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maletsidibeOS/Anos_AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="maletsidibeOS/Anos_AI") 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("maletsidibeOS/Anos_AI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use maletsidibeOS/Anos_AI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maletsidibeOS/Anos_AI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maletsidibeOS/Anos_AI", "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/maletsidibeOS/Anos_AI
- SGLang
How to use maletsidibeOS/Anos_AI 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 "maletsidibeOS/Anos_AI" \ --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": "maletsidibeOS/Anos_AI", "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 "maletsidibeOS/Anos_AI" \ --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": "maletsidibeOS/Anos_AI", "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 maletsidibeOS/Anos_AI with Docker Model Runner:
docker model run hf.co/maletsidibeOS/Anos_AI
Download inference/run.sh from maletsidibeOS/Anos_AI: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/maletsidibeOS/Anos_AI/resolve/main/inference/run.sh
- Command line
-
hf download hf://maletsidibeOS/Anos_AI/inference/run.sh
-
curl -L -o run.sh https://huggingface.co/maletsidibeOS/Anos_AI/resolve/main/inference/run.sh
1.76 kB
| # | |
| # Run the reference inference on a converted checkpoint. | |
| # | |
| # ./run.sh /path/to/DeepSeek-V4.1-Exp-TP8 | |
| # ./run.sh /path/to/DeepSeek-V4.1-Exp-TP8 examples/example_harmony.json | |
| # MP=4 ./run.sh /path/to/DeepSeek-V4.1-Exp-TP4 | |
| # | |
| # Paths inside an example are resolved from this directory, so run it from anywhere. | |
| set -euo pipefail | |
| cd "$(dirname "$0")" | |
| CKPT_PATH="${1:-${CKPT_PATH:-}}" | |
| INPUT_FILE="${2:-${INPUT_FILE:-examples/example_harmony.json}}" | |
| MP="${MP:-8}" | |
| CONFIG="${CONFIG:-config.json}" | |
| usage() { | |
| echo "usage: $0 <checkpoint-dir> [input-file]" >&2 | |
| echo >&2 | |
| echo " checkpoint-dir holds model0-mp${MP}.safetensors .. model$((MP - 1))-mp${MP}.safetensors," >&2 | |
| echo " as produced by convert.py --model-parallel ${MP}" >&2 | |
| echo " input-file TXT or JSON prompts (default: examples/example.txt)" >&2 | |
| echo >&2 | |
| echo " MP=${MP} CONFIG=${CONFIG} override with environment variables" >&2 | |
| exit 1 | |
| } | |
| [ -n "${CKPT_PATH}" ] || usage | |
| if [ ! -d "${CKPT_PATH}" ]; then | |
| echo "error: checkpoint directory not found: ${CKPT_PATH}" >&2 | |
| usage | |
| fi | |
| missing=0 | |
| for rank in $(seq 0 $((MP - 1))); do | |
| if [ ! -f "${CKPT_PATH}/model${rank}-mp${MP}.safetensors" ]; then | |
| missing=$((missing + 1)) | |
| fi | |
| done | |
| if [ "${missing}" -ne 0 ]; then | |
| echo "error: ${CKPT_PATH} is missing ${missing} of the ${MP} shards MP=${MP} needs" >&2 | |
| echo " expected model0-mp${MP}.safetensors .. model$((MP - 1))-mp${MP}.safetensors" >&2 | |
| usage | |
| fi | |
| [ -f "${INPUT_FILE}" ] || { echo "error: input file not found: ${INPUT_FILE}" >&2; usage; } | |
| torchrun --nproc-per-node "${MP}" generate.py \ | |
| --ckpt-path "${CKPT_PATH}" \ | |
| --config "${CONFIG}" \ | |
| --input-file "${INPUT_FILE}" | |