Image-Text-to-Text
Transformers
Safetensors
glm5_next
glm
exl3
tr3
vllm
sm120
nvfp4
dflash2
multimodal
shapleymcg
conversational
Eval Results (legacy)
4-bit precision
Instructions to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="brandonmusic/GLM-5.3-Flash-tr3-4bpw") 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 AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("brandonmusic/GLM-5.3-Flash-tr3-4bpw") model = AutoModelForMultimodalLM.from_pretrained("brandonmusic/GLM-5.3-Flash-tr3-4bpw", 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "brandonmusic/GLM-5.3-Flash-tr3-4bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "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/brandonmusic/GLM-5.3-Flash-tr3-4bpw
- SGLang
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw 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 "brandonmusic/GLM-5.3-Flash-tr3-4bpw" \ --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": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "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 "brandonmusic/GLM-5.3-Flash-tr3-4bpw" \ --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": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "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 brandonmusic/GLM-5.3-Flash-tr3-4bpw with Docker Model Runner:
docker model run hf.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw
Download scripts/aggregate_glm53_five_run_kld.py from brandonmusic/GLM-5.3-Flash-tr3-4bpw: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw/resolve/1ae6d70430a12d762917786696db06a7b4f9bbae/scripts/aggregate_glm53_five_run_kld.py
- Command line
-
hf download hf://brandonmusic/GLM-5.3-Flash-tr3-4bpw@1ae6d70430a12d762917786696db06a7b4f9bbae/scripts/aggregate_glm53_five_run_kld.py
-
curl -L -o aggregate_glm53_five_run_kld.py https://huggingface.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw/resolve/1ae6d70430a12d762917786696db06a7b4f9bbae/scripts/aggregate_glm53_five_run_kld.py
1.45 kB
| #!/usr/bin/env python3 | |
| """Seal the mean and dispersion of exactly five cold GLM-5.3 K4 KLD runs.""" | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| from pathlib import Path | |
| from quant_pipeline.core.artifacts import sha256_file, write_json | |
| from quant_pipeline.publication.glm53_k4_postmtp import build_five_run_kld_receipt | |
| def main() -> int: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument("--kld-report", type=Path, action="append", required=True) | |
| parser.add_argument("--output", type=Path, required=True) | |
| parser.add_argument("--execute", action="store_true") | |
| args = parser.parse_args() | |
| if len(args.kld_report) != 5: | |
| raise ValueError("pass exactly five --kld-report paths") | |
| reports = [] | |
| for path in args.kld_report: | |
| resolved = path.resolve() | |
| value = json.loads(resolved.read_text(encoding="utf-8")) | |
| if not isinstance(value, dict): | |
| raise ValueError(f"KLD report is not an object: {resolved}") | |
| reports.append((value, sha256_file(resolved))) | |
| receipt = build_five_run_kld_receipt(reports) | |
| print(json.dumps({"dry_run": not args.execute, **receipt}, sort_keys=True), flush=True) | |
| if not args.execute: | |
| return 0 | |
| output = args.output.resolve() | |
| if output.exists(): | |
| raise FileExistsError(output) | |
| write_json(output, receipt) | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |