Update README.md

README.md

@@ -1,33 +1,20 @@
----
-title: README
-emoji: 🔥
-colorFrom: blue
-colorTo: red
-sdk: static
-pinned: false
-license: mit
----
-<div align="center">
-<img src="https://raw.githubusercontent.com/wesleyscholl/squish/main/assets/squish-logo-1.png" width="330" alt="Squish" />
-
-<h3>Pre-compressed models for Apple Silicon. Load in under a second. Run fully local.</h3>
+# Squish
+Pre-compressed models for Apple Silicon. Load in under a second. Run fully local.
 
-[![GitHub](https://img.shields.io/badge/GitHub-wesleyscholl%2Fsquish-black?logo=github)](https://github.com/wesleyscholl/squish)
-[![License](https://img.shields.io/badge/license-MIT-green)](https://github.com/wesleyscholl/squish/blob/main/LICENSE)
-[![Platform](https://img.shields.io/badge/platform-Apple%20Silicon%20M1–M5-lightgrey?logo=apple)](https://github.com/wesleyscholl/squish)
-
-</div>
+[GitHub](https://github.com/konjoai/squish) &nbsp;·&nbsp; [MIT License](https://github.com/konjoai/squish/blob/main/LICENSE) &nbsp;·&nbsp; [squish.run](https://squish.run)
 
 ---
 
 ## What is this?
 
-This organization hosts models pre-compressed by [Squish](https://github.com/wesleyscholl/squish) — a local inference engine for Apple Silicon that gets models off disk and into memory in under a second.
+This organization hosts models pre-compressed by **Squish** — a local inference engine for Apple Silicon that gets models off disk and into memory in under a second.
 
 Every model here was compressed with Squish's INT4 quantization pipeline and is ready to load directly with `squish pull`. No setup, no Python environment, no cloud.
 
 ```bash
-brew install wesleyscholl/squish/squish
+brew tap konjoai/squish
+brew trust konjoai/squish
+brew install squish
 squish pull qwen3:8b
 squish run qwen3:8b
 ```
@@ -39,39 +26,37 @@ squish run qwen3:8b
 Compression takes time. A Qwen3-8B model compresses in roughly 8 minutes on an M3. You shouldn't have to wait for that on first run. Models in this org are pre-compressed and validated — pull once, load instantly every time after.
 
 | Format | What it means |
-|---|---|
-| `*-squished` | INT4-compressed, ready for `squish run` |
-| `*-squished-int8` | INT8-compressed, higher quality, larger |
+|--------|--------------|
+| `*-bf16-squished` | INT4-compressed, ready for `squish run` |
 
 ---
 
 ## Available models
 
 | Model | Squish ID | Raw size | Squished size | Context |
-|---|---|---|---|---|
+|-------|-----------|----------|---------------|---------|
 | Qwen3-8B | `qwen3:8b` | 16.4 GB | 4.4 GB | 128k |
 | Qwen3-4B | `qwen3:4b` | 8.2 GB | 2.2 GB | 32k |
-| Qwen3-1.7B | `qwen3:1.7b` | 3.5 GB | 1.0 GB | 32k |
+| Qwen3-0.6B | `qwen3:0.6b` | 1.3 GB | 0.9 GB | 32k |
 | Qwen2.5-7B-Instruct | `qwen2.5:7b` | 14.4 GB | 3.9 GB | 128k |
 | Qwen2.5-1.5B-Instruct | `qwen2.5:1.5b` | 3.1 GB | 0.9 GB | 32k |
 | Llama-3.2-3B-Instruct | `llama3.2:3b` | 6.4 GB | 1.7 GB | 128k |
+| Llama-3.2-1B-Instruct | `llama3.2:1b` | 2.5 GB | 0.7 GB | 128k |
 | Gemma-3-4B-Instruct | `gemma3:4b` | 9.8 GB | 2.6 GB | 128k |
-| DeepSeek-R1-Distill-7B | `deepseek-r1:7b` | 14.4 GB | 3.9 GB | 128k |
+| Gemma-3-1B-Instruct | `gemma3:1b` | 2.0 GB | 0.5 GB | 32k |
 
-More models added as the catalog grows. Check `squish catalog` for the full list.
+More models added as the catalog grows. Run `squish catalog` for the full list.
 
 ---
 
 ## Load time comparison (M3 16GB)
 
 | Model | Squish (INT4) | Ollama | llama.cpp |
-|---|---|---|---|
-| Qwen3-8B | **0.43s** | 4.2s | 6.1s |
-| Llama-3.2-3B | **0.33s** | 1.8s | 2.4s |
-
-*Measured cold-start on Apple M3 16GB. Results will vary by chip and storage.*
+|-------|--------------|--------|-----------|
+| Qwen3-8B | 0.43s | 4.2s | 6.1s |
+| Llama-3.2-3B | 0.33s | 1.8s | 2.4s |
 
-> ⚠️ Benchmark figures above are from internal testing. Full reproducible benchmark methodology coming soon — see [GitHub issue #benchmark](https://github.com/wesleyscholl/squish) for status.
+Measured cold-start on Apple M3 16GB. Results will vary by chip and storage.
 
 ---
 
@@ -83,7 +68,6 @@ Squish runs a local server on port 11435. Any OpenAI client works out of the box
 from openai import OpenAI
 
 client = OpenAI(base_url="http://localhost:11435/v1", api_key="squish")
-
 response = client.chat.completions.create(
     model="qwen3:8b",
     messages=[{"role": "user", "content": "Explain attention mechanisms briefly."}]
@@ -92,7 +76,7 @@ print(response.choices[0].message.content)
 ```
 
 ```bash
-# Or just point your existing tools at it
+# Or point your existing tools at it
 export OPENAI_BASE_URL=http://localhost:11435/v1
 export OPENAI_API_KEY=squish
 ```
@@ -103,22 +87,20 @@ export OPENAI_API_KEY=squish
 
 Squish uses a three-tier compression pipeline:
 
-1. **INT4 quantization** via a Rust extension (`squish_quant_rs`) with ARM NEON acceleration — 8–12 GB/s throughput on Apple Silicon
-2. **Compressed weight loader** — weights stay compressed on disk and decompress directly into Metal-mapped memory at load time
-3. **KV cache quantization** — attention cache stored at reduced precision during generation, not just weights
+- **INT4 quantization** via a Rust extension (`squish_quant_rs`) with ARM NEON acceleration — 8–12 GB/s throughput on Apple Silicon
+- **Compressed weight loader** — weights stay compressed on disk and decompress directly into Metal-mapped memory at load time
+- **KV cache quantization** — attention cache stored at reduced precision during generation, not just weights
 
 The result is a model that fits in memory on a base M3 16GB and loads faster than Ollama can parse its configuration.
 
 ---
 
-## Using models directly
-
-You can also load these models with `mlx_lm` if you want to use them outside of Squish:
+## Using models directly with mlx_lm
 
 ```python
 from mlx_lm import load, generate
 
-model, tokenizer = load("squish-community/Qwen3-8B-squished")
+model, tokenizer = load("squishai/Qwen3-8B-bf16-squished")
 response = generate(model, tokenizer, prompt="Hello", max_tokens=100)
 ```
 
@@ -128,25 +110,20 @@ response = generate(model, tokenizer, prompt="Hello", max_tokens=100)
 
 - macOS 13.0 or later
 - Apple Silicon (M1, M2, M3, M4, M5)
-- Enough unified memory for the model (check the table above)
+- Sufficient unified memory for the model (see table above)
 
-Intel Macs and Linux are not supported. Windows is not planned.
+> Intel Macs and Linux are not supported. Windows is not planned.
 
 ---
 
 ## Links
 
-- **CLI and inference engine**: [github.com/wesleyscholl/squish](https://github.com/wesleyscholl/squish)
-- **Install**: `brew install wesleyscholl/squish/squish`
-- **Issues and discussions**: [GitHub Issues](https://github.com/wesleyscholl/squish/issues)
-- **Discord**: [discord.gg/squish](https://discord.gg/FqzqeJCuh)
+- CLI and inference engine: [github.com/konjoai/squish](https://github.com/konjoai/squish)
+- Install: `brew tap konjoai/squish && brew install squish`
+- Issues and discussions: [GitHub Issues](https://github.com/konjoai/squish/issues)
 
 ---
 
-<div align="center">
-
 *Squish it. Run it. Go.*
 
-Built by [Konjo AI](https://github.com/wesleyscholl) · MIT License
-
-</div>
+Built by [Konjo AI](https://github.com/konjoai) &nbsp;·&nbsp; MIT License