Image-Text-to-Text
Transformers
Safetensors
qwen3_5
gsq
gumbel-softmax
quantization
ptq
qwen
vllm
humming
conversational
compressed-tensors
Instructions to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ") 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("ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ") model = AutoModelForMultimodalLM.from_pretrained("ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", 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 ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ
- SGLang
How to use ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ 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 "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" \ --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": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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 "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ" \ --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": "ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ", "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 ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ with Docker Model Runner:
docker model run hf.co/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ
File size: 4,865 Bytes
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import os
import re
import shutil
import sys
import tempfile
from pathlib import Path
PATCH_NAME = "qwen3_5_quantized_embedding"
def find_vllm_root() -> Path:
spec = importlib.util.find_spec("vllm")
if spec is None:
raise RuntimeError(
f"Could not find installed vLLM.\nPython executable: {sys.executable}"
)
if spec.submodule_search_locations:
return Path(next(iter(spec.submodule_search_locations))).resolve()
if spec.origin:
return Path(spec.origin).resolve().parent
raise RuntimeError("Could not determine the installed vLLM path.")
def find_qwen35_file(vllm_root: Path) -> Path:
path = vllm_root / "model_executor" / "models" / "qwen3_5.py"
if not path.is_file():
raise RuntimeError(f"Could not find Qwen3.5 implementation at:\n{path}")
return path
def already_patched(text: str) -> bool:
pattern = re.compile(
r"self\.embed_tokens\s*=\s*VocabParallelEmbedding\(\s*"
r"self\.vocab_size\s*,\s*"
r"config\.hidden_size\s*,\s*"
r"quant_config\s*=\s*self\.quant_config\s*,\s*"
r'prefix\s*=\s*f["\']\{prefix\}\.embed_tokens["\']\s*,?\s*\)',
re.DOTALL,
)
return pattern.search(text) is not None
def patch_source(text: str) -> str:
class_pos = text.find("class Qwen3_5Model")
if class_pos == -1:
raise RuntimeError("Could not find 'class Qwen3_5Model'.")
search_end = min(len(text), class_pos + 20_000)
section = text[class_pos:search_end]
pattern = re.compile(
r"(?P<indent>^[ \t]+)self\.embed_tokens\s*=\s*VocabParallelEmbedding\(\s*\n"
r"(?P=indent)[ \t]+self\.vocab_size\s*,\s*\n"
r"(?P=indent)[ \t]+config\.hidden_size\s*,\s*\n"
r"(?P=indent)\)",
re.MULTILINE,
)
match = pattern.search(section)
if match is None:
raise RuntimeError(
"Could not find the expected unpatched embedding block. "
"The installed vLLM source may have changed."
)
indent = match.group("indent")
inner = indent + " "
replacement = (
f"{indent}self.embed_tokens = VocabParallelEmbedding(\n"
f"{inner}self.vocab_size,\n"
f"{inner}config.hidden_size,\n"
f"{inner}quant_config=self.quant_config,\n"
f'{inner}prefix=f"{{prefix}}.embed_tokens",\n'
f"{indent})"
)
start = class_pos + match.start()
end = class_pos + match.end()
return text[:start] + replacement + text[end:]
def atomic_write(path: Path, content: str):
fd, tmp_name = tempfile.mkstemp(prefix=path.name + ".", suffix=".tmp", dir=path.parent)
tmp_path = Path(tmp_name)
try:
with os.fdopen(fd, "w", encoding="utf-8") as f:
f.write(content)
shutil.copymode(path, tmp_path)
os.replace(tmp_path, path)
except Exception:
tmp_path.unlink(missing_ok=True)
raise
def main():
print("=" * 70)
print("vLLM Qwen3.5 quantized embedding patch")
print("=" * 70)
print(f"Python: {sys.executable}")
vllm_root = find_vllm_root()
target = find_qwen35_file(vllm_root)
print(f"vLLM: {vllm_root}")
print(f"Target: {target}")
text = target.read_text(encoding="utf-8")
if already_patched(text):
print("[OK] Patch is already installed.")
return 0
try:
patched = patch_source(text)
except RuntimeError as exc:
print(f"[ERROR] {exc}", file=sys.stderr)
return 1
if not already_patched(patched):
print("[ERROR] Generated patch failed verification.", file=sys.stderr)
return 1
try:
compile(patched, str(target), "exec")
except SyntaxError as exc:
print(f"[ERROR] Patched source has invalid syntax: {exc}", file=sys.stderr)
return 1
backup = target.with_name(target.name + f".{PATCH_NAME}.bak")
if not backup.exists():
try:
shutil.copy2(target, backup)
print(f"Backup: {backup}")
except PermissionError:
print(f"[ERROR] Permission denied creating backup: {backup}", file=sys.stderr)
return 1
else:
print(f"Backup already exists: {backup}")
try:
atomic_write(target, patched)
except PermissionError:
print(f"[ERROR] Permission denied patching: {target}", file=sys.stderr)
return 1
final_text = target.read_text(encoding="utf-8")
if not already_patched(final_text):
print("[ERROR] Patch verification failed after writing.", file=sys.stderr)
return 1
print("[OK] Successfully patched Qwen3.5 quantized embeddings.")
print("Restart all vLLM processes before loading the model.")
print(f"Restore with: cp {backup} {target}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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