Add pebble GGUFs (f16, q8_0, q4_k_m) + README + license

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -1,20 +1,57 @@
 ---
 license: apache-2.0
-language:
-- en
-base_model:
-- basically-ai/Pebble-10M
 pipeline_tag: text-generation
 tags:
-- pebble_10m
+- gguf
+- llama.cpp
 - pebble
-- language-model
-- base-model
-- custom-code
 - mamba2
 - hybrid
 - small-language-model
-- GGUF
+base_model: basically-ai/Pebble-10M
 ---
 
-I wanted support for this model via llama.cpp. This is a preliminary model card, ready for when the I finish testing the achieved llama.cpp implementation and .gguf conversion.
+# Pebble-10M-GGUF
+
+GGUF conversions of [basically-ai/Pebble-10M](https://huggingface.co/basically-ai/Pebble-10M) (Apache 2.0).
+
+## IMPORTANT: patched llama.cpp required
+
+Pebble uses a custom hybrid Mamba2 + attention architecture. These GGUFs carry
+`general.architecture = "pebble"`, which **upstream llama.cpp refuses to load**.
+
+Everything needed to run them lives in the support repo:
+
+**[rootendpoint/basicallyai_llama.cpp_support](https://github.com/rootendpoint/basicallyai_llama.cpp_support)**
+
+- `llama.cpp-pebble.patch` - adds the `pebble` architecture to llama.cpp
+  (applies cleanly against upstream commit `0eadefe`)
+- `basicallyai_to_gguf.py` - standalone converter (numpy + safetensors only)
+- `numpy_reference.py` - independent reference implementation used to verify correctness
+
+Apply the patch, rebuild llama.cpp, then:
+
+```bash
+llama-cli -m pebble-10m-f16.gguf -p "The capital of France" -n 64
+```
+
+
+
+## Files
+
+| Quant | Size | Type |
+|-------|------|------|
+| f16 | 20.7 MB | F16 |
+| q8_0 | 11.1 MB | mostly Q8_0 |
+| q4_k_m | 7.5 MB | mostly Q4_K_M |
+
+## Verification
+
+Outputs were cross-checked token-by-token against an independent pure-numpy
+reference implementation over multiple prompts (CPU and CUDA backends, and
+quantized KV cache). Identical greedy sequences up to genuine argmax ties.
+
+## Model
+
+- 10M parameters, hidden 384, 8 layers (6 Mamba2 + 2 attention), ctx 512, vocab 2048
+- A research-scale model: expect toy-level output quality.

pebble-10m-f16-q4_k_m.gguf

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+size 7507712

pebble-10m-f16-q8_0.gguf

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+oid sha256:81c2730cc8725bb59b612905352caf44b94e92192087dcdd719f149c52d91967
+size 11104256

pebble-10m-f16.gguf

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