Instructions to use Vishva007/clef-W4A16-AutoRound-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Vishva007/clef-W4A16-AutoRound-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vishva007/clef-W4A16-AutoRound-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/clef-W4A16-AutoRound-GPTQ",
		"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/Vishva007/clef-W4A16-AutoRound-GPTQ

SGLang

How to use Vishva007/clef-W4A16-AutoRound-GPTQ 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 "Vishva007/clef-W4A16-AutoRound-GPTQ" \
    --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": "Vishva007/clef-W4A16-AutoRound-GPTQ",
		"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 "Vishva007/clef-W4A16-AutoRound-GPTQ" \
        --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": "Vishva007/clef-W4A16-AutoRound-GPTQ",
		"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 Vishva007/clef-W4A16-AutoRound-GPTQ with Docker Model Runner:
```
docker model run hf.co/Vishva007/clef-W4A16-AutoRound-GPTQ
```

Clef-W4A16-AutoRound-LLM-Compressor

Quantized W4A16 (4-bit weights, 16-bit activations) release of Cloudflare/Clef using Intel AutoRound.

Clef is a 9B multimodal decision model post-trained from Qwen3.5 that evaluates structured schemas (text, JSON, image, video) and returns calibrated probability distributions across typed questions in a single forward pass without autoregressive token generation.

Quantization Details

Method: Intel AutoRound W4A16 (sym=True, group_size=64)
Vision Tower: Preserved in native BF16 (ensures zero OCR/visual degradation)
Joint Schema Head (joint_head.safetensors): Preserved in native BF16
Linear Convolutions: Preserved in native BF16 to prevent drift in Qwen3.5 linear attention layers
Accuracy: Zero loss observed across text, JSON state, and multimodal test suites compared to the base checkpoint.

Quickstart

vLLM Serving (`/v1/systemone`)

Requires vllm > v0.31.0 or vllm/vllm-openai:nightly.

1. Launch Server

docker run --gpus all -p 8000:8000 --ipc=host vllm/vllm-openai:nightly \
  vllm serve Vishva007/clef-W4A16-AutoRound \
    --port 8000 --host 0.0.0.0 \
    --max-model-len 32k --max-num-batched-tokens 32k --max-num-seqs 128 \
    --kv-cache-dtype fp8 --gpu-memory-utilization 0.90 \
    --enable-prefix-caching --enable-chunked-prefill \
    --trust-remote-code --dtype bfloat16

2. Client Example (`typesafe_sdk`)

pip install typesafe_sdk

from typesafe_sdk import Choice, TypeSafeClient

with TypeSafeClient(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1",  # or your proxy URL
    model="Vishva007/clef-W4A16-AutoRound-GPTQ",
) as client:
    res = client.system_one(
        state="Checkout throwing 504 Gateway Timeouts. Database latency at 4,000ms.",
        questions={
            "severity": Choice(
                instructions="Assess severity",
                criteria={"sev1": "Total outage", "sev2": "Degraded", "sev3": "Minor"},
            ),
            "team": Choice(
                instructions="Route to team",
                criteria={"sre": "Infrastructure", "billing": "Payments"},
            ),
        },
    )

    for k, v in res.choices.items():
        print(f"{k}: {v.choice} ({v.confidence:.1%})")

Installation

pip install torch transformers huggingface_hub pillow

Usage (`systemone` API)

import sys
from huggingface_hub import snapshot_download

# Download repo and load custom joint schema model
repo_id = "Vishva007/clef-W4A16-AutoRound-GPTQ"  # or auto-gptq / llm-compressor variant
model_path = snapshot_download(repo_id)
sys.path.insert(0, model_path)

from joint_schema_model import load_release_model, systemone

model, processor = load_release_model(model_path, device="cuda")

# 1. Text / JSON Decision Example
response = systemone(model, processor, {
    "model": "clef",
    "state": "Prod database latency spiked to 4,000ms. Checkout failing with 504 Gateway Timeouts.",
    "questions": {
        "severity": {
            "type": "choice",
            "instructions": "Determine incident severity level",
            "criteria": {
                "SEV_1": "Critical revenue outage",
                "SEV_2": "Major feature degradation",
                "SEV_3": "Minor issue"
            }
        },
        "urgency": {
            "type": "score",
            "instructions": "Urgency rating",
            "criteria": ["Low", "Medium", "Immediate page"]
        },
        "rollback": {
            "type": "noul",
            "instructions": "Should a rollback be initiated?"
        }
    }
})

print(response["answers"])

Multimodal (Image) Evaluation

from PIL import Image

response = systemone(model, processor, {
    "model": "clef",
    "state": "Review the uploaded invoice receipt.",
    "images": [Image.open("receipt.png")],
    "questions": {
        "legible": {"type": "noul", "instructions": "Is the receipt text clear and legible?"},
        "amount_exceeds_1000": {"type": "noul", "instructions": "Is total > $1000 USD?"}
    }
})

print(response["answers"])

Installation

pip install torch transformers huggingface_hub pillow

Usage (`systemone` API)

import sys
from huggingface_hub import snapshot_download

# Download repo and load custom joint schema model
repo_id = "Vishva007/Clef-W4A16-AutoRound"  # or auto-gptq / llm-compressor variant
model_path = snapshot_download(repo_id)
sys.path.insert(0, model_path)

from joint_schema_model import load_release_model, systemone

model, processor = load_release_model(model_path, device="cuda")

# 1. Text / JSON Decision Example
response = systemone(model, processor, {
    "model": "Clef",
    "state": "Prod database latency spiked to 4,000ms. Checkout failing with 504 Gateway Timeouts.",
    "questions": {
        "severity": {
            "type": "choice",
            "instructions": "Determine incident severity level",
            "criteria": {
                "SEV_1": "Critical revenue outage",
                "SEV_2": "Major feature degradation",
                "SEV_3": "Minor issue"
            }
        },
        "urgency": {
            "type": "score",
            "instructions": "Urgency rating",
            "criteria": ["Low", "Medium", "Immediate page"]
        },
        "rollback": {
            "type": "noul",
            "instructions": "Should a rollback be initiated?"
        }
    }
})

print(response["answers"])

Multimodal (Image) Evaluation

from PIL import Image

response = systemone(model, processor, {
    "model": "Clef",
    "state": "Review the uploaded invoice receipt.",
    "images": [Image.open("receipt.png")],
    "questions": {
        "legible": {"type": "noul", "instructions": "Is the receipt text clear and legible?"},
        "amount_exceeds_1000": {"type": "noul", "instructions": "Is total > $1000 USD?"}
    }
})

print(response["answers"])

Formats Available

Repository	Format	Engine Target
`Vishva007/Clef-W4A16-AutoRound`	AutoRound / AutoGPTQ	Transformers / Native Python
`Vishva007/Clef-W4A16-AutoRound-GPTQ`	AutoGPTQ Standard	Transformers / ExLlama / AutoGPTQ
`Vishva007/Clef-W4A16-AutoRound-LLM-Compressor`	Compressed-Tensors	vLLM / SGLang

🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

PyTorch 2.14

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.14 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.14-runpod`	`d7lxsa4w9m`
PyTorch 2.14 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.14-runpod`	`yk0y6j6rpg`
PyTorch 2.14 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.14-runpod`	`gsp4gwx0nw`

PyTorch 2.13

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.13 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.13-runpod`	`gmlupxnxfk`
PyTorch 2.13 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.13-runpod`	`y3j8xvk4f4`
PyTorch 2.13 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.13-runpod`	`vigpissn5w`

PyTorch 2.12

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.12 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.12-runpod`	`ctmz86zmf0`
PyTorch 2.12 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.12-runpod`	`qjko5yiwzi`
PyTorch 2.12 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.12-runpod`	`ifg6xmye0f`

Acknowledgements

Base model by Cloudflare.
Quantization powered by Intel AutoRound.

Downloads last month: 124

Safetensors

Model size

3B params

Tensor type

BF16

I32

Model tree for Vishva007/clef-W4A16-AutoRound-GPTQ

Base model

Qwen/Qwen3.8-27B

Finetuned

Cloudflare/clef

Quantized

(35)

this model

Collection including Vishva007/clef-W4A16-AutoRound-GPTQ

Clef & Clef-Flash

Collection

Quantized Clef and Clef-Flash models for efficient image-text decision models (AutoRound W4A16) • 4 items • Updated 1 day ago

Clef-W4A16-AutoRound-LLM-Compressor

Quantization Details

Quickstart

vLLM Serving (/v1/systemone)

1. Launch Server

2. Client Example (typesafe_sdk)

Installation

Usage (systemone API)

Multimodal (Image) Evaluation

Installation

Usage (systemone API)

Multimodal (Image) Evaluation

Formats Available

🚀 Deploy on RunPod

PyTorch 2.14

PyTorch 2.13

PyTorch 2.12

Acknowledgements

Model tree for Vishva007/clef-W4A16-AutoRound-GPTQ

Collection including Vishva007/clef-W4A16-AutoRound-GPTQ

vLLM Serving (`/v1/systemone`)

2. Client Example (`typesafe_sdk`)

Usage (`systemone` API)

Usage (`systemone` API)