Image-Text-to-Text
Transformers
Safetensors
English
Chinese
Korean
internvl
internvl3.5
vision-language
multimodal
vllm
compressed-tensors
fp8
w8a16
ampere
wsl2
conversational
Instructions to use hsmin92/internvl35-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsmin92/internvl35-fp8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hsmin92/internvl35-fp8") 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 AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hsmin92/internvl35-fp8") model = AutoModelForMultimodalLM.from_pretrained("hsmin92/internvl35-fp8", device_map="auto") 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?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hsmin92/internvl35-fp8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hsmin92/internvl35-fp8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hsmin92/internvl35-fp8", "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/hsmin92/internvl35-fp8
- SGLang
How to use hsmin92/internvl35-fp8 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 "hsmin92/internvl35-fp8" \ --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": "hsmin92/internvl35-fp8", "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 "hsmin92/internvl35-fp8" \ --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": "hsmin92/internvl35-fp8", "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 hsmin92/internvl35-fp8 with Docker Model Runner:
docker model run hf.co/hsmin92/internvl35-fp8
File size: 1,902 Bytes
e8805c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env bash
set -euo pipefail
failures=0
check_command() {
local name="$1"
if command -v "$name" >/dev/null 2>&1; then
echo "OK command: $name -> $(command -v "$name")"
else
echo "FAIL command: $name"
failures=$((failures + 1))
fi
}
check_file() {
local path="$1"
if [[ -f "$path" ]]; then
echo "OK file: $path"
else
echo "FAIL file: $path"
failures=$((failures + 1))
fi
}
echo "===== COMMANDS ====="
check_command python
check_command vllm
check_command gcc
check_command g++
check_command ninja
check_command nvidia-smi
echo
echo "===== SYSTEM FILES ====="
check_file /usr/include/python3.12/Python.h
check_file /usr/lib/wsl/lib/libcuda.so
echo
echo "===== PYTHON PACKAGES ====="
python - <<'PY'
import importlib.metadata
for package in ("vllm", "torch", "transformers", "compressed-tensors", "nvidia-cuda-nvrtc"):
try:
print(f"OK {package}: {importlib.metadata.version(package)}")
except importlib.metadata.PackageNotFoundError:
print(f"MISS {package}")
PY
echo
echo "===== NVRTC FILES ====="
python - <<'PY'
import site
from pathlib import Path
found = []
for site_dir in site.getsitepackages():
for path in (Path(site_dir) / "nvidia").glob("cu*/lib/libnvrtc*.so*"):
found.append(path)
for path in sorted(found):
print(path)
if not found:
raise SystemExit("No NVRTC libraries found")
PY
echo
echo "===== CUDA DRIVER LINK ====="
cat >/tmp/internvl_cuda_link_test.cpp <<'CPP'
extern "C" int cuInit(unsigned int flags);
int main() { return 0; }
CPP
LIBRARY_PATH="/usr/lib/wsl/lib:${LIBRARY_PATH:-}" \
g++ /tmp/internvl_cuda_link_test.cpp -Wl,--no-as-needed -lcuda \
-o /tmp/internvl_cuda_link_test
echo "OK CUDA driver link"
if [[ "$failures" -ne 0 ]]; then
echo "Runtime check failed: $failures required item(s) missing." >&2
exit 1
fi
echo
echo "WSL RUNTIME CHECK: SUCCESS"
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