Reading Room

Over Alderley and Birtles Reading Room has served the local community for more than 120 years. It is an ideal location for events and functions. It is a charming community meeting place that seats up to 80 people.

Event Space

The Reading Room consists of a large main room, a smaller room and kitchen as well as storeroom and toilets. Main room: 14.4m x 5.94m. Height 3.8m rising to 5m in the centre Small room: 4.3m x 4m. Height 3.8m The Reading Room seats a maximum of 80 people. Furniture Included in hire of the Reading Room is: 12 large tables: 1830 x 685 x 698 high 12 small tables: 915 x 685 x 698 high 72 upholstered chairs Additional chairs available on request

KITCHEN

Included in hire of the Reading Room is the kitchen which includes: 2 x cookers Dishwasher Water boiler Double sink Extensive work surfaces Under-counter fridge Suitable for up to 80 people: Cups Saucers Main plates Side plates Wine glasses Bowls Jugs Teapots Coffee pots Sugar bowls Tablecloths Cutlery Hirers wishing to use the kitchen equipment, crockery and cutlery must advise the Booking Secretary at the time of booking. A charge will be made for any damages or breakages.

parking

The Reading Room has 30 car parking spaces split across two car parks, one either side of the building. Additional cars can be parked on the grass in good weather.

toilets

2 x ladies 2 x gents 1x disabled

entertainment

The Reading Room does not have an entertainment licence so hirers must ensure they comply with current legislation in this respect. A musical entertainment must cease by 12.00 midnight and discos and bands should be asked to control the volume to an acceptable level taking into consideration the proximity of neighbours.

events for under 18s

In the case of functions organised for those under the age of 18 it is the responsibility of the organiser to ensure that no alcoholic beverages or banned substances are available on the premises. Event organisers must ensure that at least two attendants at the age of 18 or over are available to prevent guests from congregating in the entrance area and to supervise any emergency evacuations.

health and safety

Smoke generators, lasers, strobe lights, pyrotechnics and naked lights including candles are prohibited.

Cleaning

The Reading Room should be left in a clean and tidy state including removal of rubbish as there are no bins available at the Reading Room.

contact us

Over Alderley and Birtles Reading Room, Birtles Lane, Over Alderley, Cheshire, SK10 4SN For Booking and Enquiries please call: 01625 478703 or email: bookings@overalderleyvillagehall.co.uk

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Quick Run Qwen3.5-27B-FP8 Locally (No Cloud) Full Speed NPU Mode Dummy Proof Guide

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Quick Run Qwen3.5-27B-FP8 Locally (No Cloud) Full Speed NPU Mode Dummy Proof Guide

Setting up this model locally is incredibly fast if you use the native CMD prompt.

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📤 Release Hash: 4ffcab4c8d613f6e259922f80224b4e8 • 📅 Date: 2026-06-25
  • CPU: 8-core / 16-thread recommended for orchestration
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The Qwen3.5-27B-FP8 is a state-of-the-art language model featuring 27 billion parameters and FP8 quantization for efficient inference. It delivers high performance with reduced memory footprint, enabling real-time applications on consumer‑grade hardware. Benchmarks show superior accuracy on reasoning tasks while maintaining low inference latency compared to similar‑sized models. The model supports mixed‑precision training, allowing developers to fine‑tune on standard GPUs without specialized hardware. Its architecture incorporates advanced attention mechanisms and robust safety alignments, making it suitable for enterprise and research deployments.

Specification Value
Parameters 27 B
Quantization FP8
Training Data Web‑scale corpus
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How to Setup gemma-4-12B-it-qat-w4a16-ct Local Guide

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How to Setup gemma-4-12B-it-qat-w4a16-ct Local Guide

If you want the fastest local installation for this model, use Docker.

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🛠 Hash code: 204dd5a592c5cb6de8735e3eb5d0b83e — Last modification: 2026-06-27
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The **gemma-4-12B-it-qat-w4a16-ct** model represents a significant advancement in instruction‑tuned language models, combining a 12‑billion parameter base with a specialized QAT quantization scheme. It leverages a *w4a16* format, meaning weights are stored in 4‑bit precision while activations remain in 16‑bit floating point, delivering a balanced trade‑off between memory footprint and computational accuracy. The model has been optimized through **QAT**, which fine‑tunes the network to mitigate quantization errors and preserve performance across diverse tasks. In benchmark evaluations, it consistently outperforms comparable 12B‑parameter models while requiring roughly 60 % less GPU memory, making it ideal for deployment on resource‑constrained edge devices. A quick reference table below compares its key attributes with other popular Gemma variants, highlighting its superior efficiency and accuracy metrics.

Model **gemma-4-12B-it-qat-w4a16-ct**
Parameters 12 B
Quantization w4a16 (QAT)
Memory Usage ~60 % less than baseline 12B models
Accuracy Higher than comparable 12B variants
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📘 Build Hash: 43086716c5c79af28f5e352cfb4a18d1 • 🗓 2026-06-26
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📄 Hash Value: 1851232b8adb925a8d7153e096e079c2 | 📆 Update: 2026-06-24
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How to Install chandra-ocr-2 For Beginners

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How to Install chandra-ocr-2 For Beginners

To install this model locally in the shortest time, opt for Docker.

Follow the sequence of steps detailed below.

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🔍 Hash-sum: d1d2e3bc7db1e49906b9fc503f4bedff | 🕓 Last update: 2026-06-23
  • Processor: 6-core 3.5 GHz minimum required
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  • Disk: high-speed SSD 120 GB to cache model layers
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The **chandra-ocr-2** model delivers *state-of-the-art* optical character recognition with unprecedented accuracy across diverse document types. It leverages a deep convolutional neural network architecture combined with attention mechanisms to capture both fine-grained character shapes and contextual layout cues. The model supports a wide range of languages and scripts, making it suitable for global enterprise workflows. Performance benchmarks show a character error rate below 0.5% on standard benchmarks, outperforming previous generations by over 15%. Integration is streamlined via a lightweight API that processes images in *real-time* with minimal hardware requirements.

Specification Value
Model size 210 MB
Supported languages 100
Input resolution 2048 × 3072 px
Processing speed > 30 fps
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