Instructions to use djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700")
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("djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700")
model = AutoModelForMultimodalLM.from_pretrained("djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700", 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 djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700",
		"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/djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700

SGLang

How to use djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700 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 "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700" \
    --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": "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700",
		"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 "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700" \
        --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": "djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700",
		"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 djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700 with Docker Model Runner:
```
docker model run hf.co/djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700
```

GLM-5.3-Flash · RFA + RFI8 composite quant (8× R9700 / RDNA4)

A self-quantized derivative of zai-org/GLM-5.3-Flash, tuned for 8× AMD Radeon R9700 (gfx1201 / RDNA4)

Model card

This is a composite-quantized checkpoint of Z.ai's GLM-5.3-Flash — the first natively multimodal model in the GLM-5 series (321B total / ~18B active parameters). It was quantized from the official FP8 release using the tcclaviger/vllm composite quantizer and is intended to be served with vLLM on RDNA4 (AMD Radeon R9700) hardware.

📦 Companion serving repo: GLM-5.3-Flash-rocm-r9700 — the ROCm/RDNA4 overlay + quantization recipe.

Benchmark

Configuration	GPQA Diamond	Correct	Empty	Answered
GLM-5.3-Flash (Z-AI API)	80.8%	76	6	94
GLM-5.3-Flash-RFA-RFI8	85.1%	80	6	94

Details

Reasoning Effort: high
Max Output Len: 32k Tokens

Total Context Limit for each task in test 32k, means 6x tasks use more than 32k output tokens

Serving performance (8× R9700, FY2026-09 production config)

Scenario	Throughput
Decode, batch size 1, MTP OFF	~34–39 tok/s
Decode, batch size 1, MTP spec=3	~82–88 tok/s
Aggregate, 4 concurrent, MTP spec=3	~155 tok/s
Context window (fp8 KV)	300,000 tokens

MTP speculative decoding: mean acceptance length ~3.7–3.9 of 4 draft tokens, average draft acceptance 91–97% (live engine metrics, GPQA-style prompts).

Attribution & credits
Quantization
Quick start
Model details
Multimodal policy
Known limitations
License

Attribution & credits

Component	Credit
Base model	zai-org/GLM-5.3-Flash (Z.ai, MIT)
Quantizer & quant kernels (RFA / RFI)	`tcclaviger/vllm:latest` (IronLLM Labs)
RDNA4 port + quant recipe	GLM-5.3-Flash-rocm-r9700 (this project's overlay)

The composite RFA + RFI8 quantization scheme, its kernels, and the serving runtime are provided by tcclaviger/vllm (IronLLM Labs). This checkpoint is the result of applying a quantization recipe (archspec + source patches + kda-remap, in the companion repo) on top of that stack, and a port of the GLM-5.3-Flash architecture to the RDNA4 serving path.

Quantization

The checkpoint mixes three schemes in one quant_method: "rfi" composite:

Scheme	Bits	Applied to	Stored size
RFA	4.5 bpw	MoE routed experts (42 layers × 288 experts)	~171.3 GB
RFI8	8 bpw	attention / shared-expert / dense linears	~7.8 GB
BF16 / FP32	16 / 32 bpw	embeddings, vision tower, norms, MTP layer	~18.7 GB

Average bits-per-weight

bpw = (total safetensors bytes × 8) / total parameters
    = (197,843,715,288 × 8) / 321,342,220,638
    = 4.9254  ≈  4.93 bpw

Metric	Value
Total parameters	321,342,220,638 (~321.3B)
On-disk size	197.8 GB · 25 safetensors shards
Average bpw	4.9254 ≈ 4.93
vs. FP8 source	0.60× (197.8 GB vs 328.3 GB)
vs. BF16	0.31× (197.8 GB vs 642.7 GB)

Quick start

docker pull tcclaviger/vllm:latest

git clone https://huggingface.co/djdeniro/GLM-5.3-Flash-rocm-r9700 overlay

# current production config (MTP spec=3, fp8 KV, 300k context)
docker run --rm --tty --ipc=host --shm-size=128g \
  --device /dev/kfd:/dev/kfd --device /dev/dri:/dev/dri \
  -v /path/to/GLM-5.3-Flash-RFA-RFI8-8xR9700:/models:ro \
  -v "$PWD/overlay":/overlay:ro \
  --entrypoint bash tcclaviger/vllm:latest \
  -c "/overlay/apply_overlay.sh && GLM5_NEXT_MTP_PROPOSER=1 exec vllm serve /models \
      --served-model-name glm53-flash --trust-remote-code --quantization rfi \
      --tensor-parallel-size 8 --gpu-memory-utilization 0.9575 \
      --max-model-len 300000 --max-num-seqs 4 --max-num-batched-tokens 2048 \
      --kv-cache-dtype fp8 \
      --speculative-config '{\"method\":\"mtp\",\"num_speculative_tokens\":3}' \
      --enable-prefix-caching --distributed-executor-backend mp \
      --compilation-config '{\"cudagraph_capture_sizes\":[1,2,4,8,16],\"cudagraph_mode\":\"FULL_AND_PIECEWISE\",\"cudagraph_copy_inputs\":true}'"

Model details

Property	Value
Architecture	`Glm5NextForConditionalGeneration`
Layers	45 = 34 KDA (linear attention) + 11 DSA (sparse-MLA)
Routed experts	288 (top-8) + 1 shared expert
Extra	mHC hyper-connections, 1 nextn MTP draft layer, native vision tower
Context (fp8 KV)	300,000 tokens

Multimodal policy

Images are resized with aspect ratio preserved, clamped to min 384×384 / max 1280×1280, and fed with a min/max image-token budget. The model accepts image and video inputs natively.

Known limitations

fp8 KV without runtime calibration — serve with --kv-cache-dtype fp8 and scales fixed at 1.0. Do not enable --calculate-kv-scales: runtime calibration on the profile dummy-run produces garbage scales from the uninitialized KDA recurrent state (details in the serving repo).
The 300k context / MTP spec=3 config presumes the VRAM headroom of the 256 GB 8× R9700 node.
Chat needs reasoning_effort="low" — the default Reasoning Effort Max spends 16k+ tokens thinking before producing content on long generations.

License

Downloads last month: 46

Safetensors

Model size

169B params

Tensor type

BF16

F32

Model tree for djdeniro/GLM-5.3-Flash-RFA-RFI8-8xR9700

Base model

zai-org/GLM-5.3-Flash

Quantized

(99)

this model

djdeniro
/

GLM-5.3-Flash-RFA-RFI8-8xR9700