Clef-Flash-W4A16-AutoRound-LLM-Compressor

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

Clef-Flash 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.

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Quantization Details

• Method: Intel AutoRound W4A16 (sym=True, group_size=32)
• 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.

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Quickstart

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-flash-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-flash",
    "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-flash",
    "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"])

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Formats Available

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

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🚀 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	Deploy
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	Deploy
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	Deploy
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

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Acknowledgements

• Base model by Cloudflare.
• Quantization powered by Intel AutoRound.