SHADOW 50M Instruct v1.1

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README.md

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+---
+license: mit
+language:
+- en
+library_name: shadow
+pipeline_tag: text-generation
+tags:
+- small-language-model
+- cpu
+- retrieval
+- memory-on-disk
+- ternary
+- offline
+- edge
+- shadow
+---
+
+<p align="center"><img src="shadow.png" alt="SHADOW" width="260"></p>
+
+<h1 align="center">SHADOW 50M Instruct</h1>
+
+<p align="center">44M parameters · ternary · exact circuits inside the model · 100M-token archive on disk · 19.8 MB · CPU, offline</p>
+
+A small language model that runs on a CPU at about 2,000 tokens a second in 40 MB of RAM, with no network and no
+framework. It computes arithmetic, dates, units, counting and sorting with circuits inside its weights, and it keeps
+records on disk as its own memory, quoting the record it read. Built from scratch; the weights are ternary and the
+vocabulary table is frozen.
+
+The full technical read, the kernels for Windows and Linux, the browser page, the harnesses and every measurement are
+on GitHub: **[github.com/QLNI/SHADOW-50M-Instruct](https://github.com/QLNI/SHADOW-50M-Instruct)**. Please start there.
+
+## Downloads
+
+| file | what it is | size |
+|---|---|---|
+| [**shadow50_instruct.shdw**](https://huggingface.co/QLNI/shadow-50m-instruct/resolve/main/shadow50_instruct.shdw?download=true) | the deployment container, version 1.1: ternary weights, the frozen vocabulary table, runtime constants, the self-test prompt. Drop it into `deployment/` of the GitHub repository and run `python shadow_chat.py`. | 19.8 MB |
+| [**shadow50_instruct.pt**](https://huggingface.co/QLNI/shadow-50m-instruct/resolve/main/shadow50_instruct.pt?download=true) | the master weights, for fine-tuning with the kit in the repository (`finetune/`). | 310 MB |
+
+## Try it in your browser
+
+[qlni.github.io/SHADOW-50M-Instruct/web](https://qlni.github.io/SHADOW-50M-Instruct/web/): the same kernel compiled to
+WebAssembly, the 19.8 MB model fetched once, nothing installed.
+
+## Licence
+
+MIT. If it turns out useful, a mention of SHADOW somewhere in your work would be appreciated.
+
+© QLNI 2026

config.json

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+{
+  "model_type": "shadow",
+  "architectures": ["SHADOW50MInstruct"],
+  "model_name": "SHADOW 50M Instruct",
+  "version": "1.1",
+  "num_parameters": 44100000,
+  "hidden_size": 896,
+  "num_hidden_layers": 6,
+  "num_attention_heads": 14,
+  "num_key_value_heads": 2,
+  "head_dim": 64,
+  "intermediate_size": 2464,
+  "hidden_act": "silu",
+  "vocab_size": 73880,
+  "vocab_code_bits": 512,
+  "vocab_trainable": false,
+  "weight_format": "ternary",
+  "bits_per_weight": 1.58,
+  "max_position_embeddings": 8192,
+  "archive_max_tokens": 100000000,
+  "archive_bytes_per_token": 288,
+  "deployment_file": "shadow50_instruct.shdw",
+  "deployment_bytes": 19799680,
+  "runtime": "shadow50 kernel, C, AVX2 FMA F16C, CPU",
+  "license": "mit",
+  "homepage": "https://github.com/QLNI/SHADOW-50M-Instruct"
+}

shadow50_instruct.pt

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