Instructions to use Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix with Transformers:

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

pipe = pipeline("text-generation", model="Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix")
model = AutoModelForCausalLM.from_pretrained("Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix

SGLang

How to use Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix 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 "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix" \
    --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": "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix" \
        --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": "Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix
```

GLM-5.3-DERISKED-Int4-Int8Mix

Full GLM-5.3 MoE · Tech2wild Int4-Int8Mix quant · Blackfrost DWM

Built by Blackfrost · Las Vegas, Nevada

Public and ungated. Same derisked weights Blackfrost applied on top of Tony / Tech2wild's Int4-Int8Mix quant.

Credit — Tech2wild

This checkpoint starts from Tech2wild/GLM-5.3-Int4-Int8Mix by Tony (@Tech2Wild / tonyd2wild). That is the Int4-Int8Mix compressed-tensors quant of full GLM-5.3 that actually fits 4× NVIDIA DGX Spark. The Hub base model for this repo is that Tech2wild artifact.

Recipe, serving notes, and Spark TP4 numbers for the stock quant live in Tony's writeup: tonyd2wild/GLM-5.3-Int4-Int8Mix-TP4-4x-DGX-Spark.

Original architecture / license parent: zai-org/GLM-5.3 (Z.AI). Quant format: Neural Magic / Red Hat compressed-tensors. Layer map: QuantTrio-style Int4 experts / Int8 dense+attention, as reproduced by Tech2wild.

Blackfrost did not re-quantize these weights. We copied the Tech2wild tree and applied an independent direction-weight modification (DWM) pass. Stock Tech2wild shards were not overwritten.

What this is

Independent Int4-Int8Mix pack-quantized safetensors of GLM-5.3 (full ~743B MoE / ~40B active, not Flash) after a Blackfrost DWM pass.

The intended behavior is in the weights. Production DWM details are proprietary and are not disclosed beyond the locked recipe below.

This artifact has not been through a judged refusal suite. Do not copy NVFP4 / BF16 / Flash GGUF refusal percentages onto this checkpoint.

Specifications


Architecture	`GlmMoeDsaForCausalLM` (`glm_moe_dsa`)
Quant source	`Tech2wild/GLM-5.3-Int4-Int8Mix` · rev `206507bbb047d8223964a0414cd83230c59428f9`
Quant	`compressed-tensors` `pack-quantized` · int8 group-128 linears · int4 group-128 routed experts · layer 0 BF16 · MTP int8 channel (stock Tech2wild)
Shards	282 safetensors
Layers	78 main + MTP layer 78
Hidden size	6144
Experts	256 routed · top-8 / token · 1 shared
DWM	alpha 3.0 · skip-early 2 · one pass · frozen scales · no norm restore
Targets	76 `o_proj` + 1 dense `down_proj` + 19200 expert `down_proj` + 75 shared `down_proj`
Runtime	vLLM `compressed-tensors` / pack-quantized (multi-Spark). Not a GGUF.
Languages	English and Chinese
Access	Public, ungated

Serving

Same vLLM compressed-tensors path as the Tech2wild stock quant. GB10 / sm121 still needs the Spark kernel overlays Tony documents; on standard CUDA, --quantization compressed-tensors is the load flag.

vllm serve Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix \
  --quantization compressed-tensors \
  --kv-cache-dtype fp8 \
  --tensor-parallel-size 4 --enable-expert-parallel \
  --served-model-name glm-5.3-derisked \
  --max-model-len 131072

Disclaimer

This checkpoint has a deliberately altered refusal profile and is intended for research and local use. It is not a safety boundary. Outputs may be inaccurate, offensive, unsafe, or otherwise unsuitable. Operators are responsible for lawful use and application-level controls.

License

Inherits the GLM-5.3 license from Z.AI. Quantization and DWM change weights only, not the license terms. Preserve upstream copyright and license notices.

Contact

@Blackfrost_AI on X · Blackfrost · Las Vegas, Nevada

Downloads last month: 433

Safetensors

Model size

779B params

Tensor type

I32

BF16

F32

Model tree for Blackfrost-AI/GLM-5.3-DERISKED-Int4-Int8Mix

Base model

zai-org/GLM-5.3

Quantized

Tech2wild/GLM-5.3-Int4-Int8Mix

Finetuned

(1)

this model