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@@ -48,3 +48,4 @@ gemma-4-E2B-it-int4-aggr-v5/gemma-4-E2B-it-int4-aggr-v5.cellmd filter=lfs diff=l
 gemma-4-E2B-it-int4-aggr-v5/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 gemma-4-E2B-it-int4-aggr-v2/gemma-4-E2B-it-int4-aggr-v2.cellmd filter=lfs diff=lfs merge=lfs -text
 gemma-4-E2B-it-int4-aggr-v2/gemma-4-E2B-it-int4-aggr-v2.tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Bonsai-1.7B_v2/Bonsai-1.7B_v2.cellm filter=lfs diff=lfs merge=lfs -text

Bonsai-1.7B_v2/Bonsai-1.7B_v2.cellm

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+version https://git-lfs.github.com/spec/v1
+oid sha256:131e932a24e8e75f9baa6aa572d1ccb2bde442da0fa68d688df67f231a01ae69
+size 242149312

Bonsai-1.7B_v2/README.md

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+# Bonsai 1.7B (1-Bit Quantized)
+
+Bonsai 1.7B is an experimental 1-bit quantized Large Language Model. It uses a specialized `Q1_0_g128` format that achieves approximately 1.125 bits per parameter.
+
+## Model Details
+
+- **Parameters**: 1.7 Billion
+- **Format**: `.cellm` (Cellm binary format)
+- **Quantization**: 1-bit sign-magnitude with 16-bit group scales (g128)
+- **Size**: 231 MB
+- **Base Architecture**: Qwen2-style Transformer
+
+## Usage in Cellm
+
+To run inference using the Cellm CLI:
+
+```bash
+./target/release/infer \
+  --model Bonsai-1.7B_v2.cellm \
+  --tokenizer tokenizer.json \
+  --prompt "What is sycophancy?" \
+  --backend metal \
+  --gen 100
+```
+
+## Performance Note
+
+This model is optimized for extremely low-memory environments. At 231MB, it can run on devices with very limited RAM. While the quantization is aggressive, it maintains coherent English generation for simple prompts.
+
+## Implementation Analysis
+
+For a detailed technical breakdown of how the 1-bit quantization works and how it was implemented in cellm, see the [Bonsai 1-Bit Analysis](https://github.com/jeffasante/cellm/blob/main/docs/bonsai_1bit_analysis.md).