Add SmolLM2-135M-Instruct-LiteRT GGUF Q4_K_M conversion

.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
+SmolLM2-135M-Instruct-LiteRT_Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

DONE.txt

@@ -0,0 +1,2 @@
+converted 2026-05-06 06:08:19
+size_mb: 105

README.md

@@ -0,0 +1,104 @@
+---
+    language:
+    - en
+    tags:
+    - duoneural
+    - litert
+    - edge
+    - gguf
+    - on-device
+    - smollm
+- smol
+- tiny
+- litert
+- edge
+- instruct
+    base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+    pipeline_tag: text-generation
+    license: apache-2.0
+    ---
+
+    # SmolLM2-135M-Instruct-LiteRT
+
+    **SmolLM2 135M Instruct — ultra-tiny on-device assistant (~90MB)** — converted for mobile and edge deployment by [DuoNeural](https://huggingface.co/DuoNeural).
+
+    - **Source model:** [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
+    - **Format:** GGUF Q4_K_M (llama.cpp-compatible)
+    - **File size:** 105 MB
+    - **Quantization:** 4-bit K-mean (Q4_K_M) — excellent accuracy/size trade-off for edge devices
+    - **Target platforms:** Android, iOS, desktop edge inference
+    - **Converted:** 2026-05-06 06:08:19 by Archon / DuoNeural
+
+    ## Usage
+
+    ### llama.cpp (CLI)
+    ```bash
+    ./llama-cli -m SmolLM2-135M-Instruct-LiteRT_Q4_K_M.gguf -n 512 --temp 0.7
+    ```
+
+    ### Google AI Edge / MediaPipe (Android/iOS)
+    This GGUF is compatible with [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [llama.cpp Android bindings](https://github.com/ggerganov/llama.cpp) for on-device inference. For use with [Google Edge Gallery](https://ai.google.dev/edge/gallery), convert to `.task` bundle using MediaPipe LLM conversion tools.
+
+    ### Python via llama-cpp-python
+    ```python
+    from llama_cpp import Llama
+
+    llm = Llama(
+        model_path="SmolLM2-135M-Instruct-LiteRT_Q4_K_M.gguf",
+        n_ctx=2048,
+        n_threads=4,
+        verbose=False,
+    )
+
+    response = llm.create_chat_completion(
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello! How can you help me today?"},
+        ]
+    )
+    print(response["choices"][0]["message"]["content"])
+    ```
+
+    ### Ollama
+    ```bash
+    ollama run hf.co/DuoNeural/SmolLM2-135M-Instruct-LiteRT
+    ```
+
+    ## About the Conversion
+
+    Converted using [llama.cpp](https://github.com/ggerganov/llama.cpp) GGUF pipeline with CUDA acceleration.
+    Source weights downloaded from HuggingFace, converted to F16 GGUF, then quantized to Q4_K_M.
+
+    ---
+
+    ## DuoNeural
+
+    **DuoNeural** is an open AI research lab — human + AI in collaboration.
+
+    | Platform | Link |
+    |----------|------|
+    | HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
+    | Website | [duoneural.com](https://duoneural.com) |
+    | GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
+    | X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
+    | Email | duoneural@proton.me |
+    | Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
+    | Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
+
+    ### DuoNeural Research Publications
+
+    | Title | DOI |
+    |-------|-----|
+    | [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
+    | [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
+    | [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
+    | [The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems](https://doi.org/10.5281/zenodo.19952612) | [10.5281/zenodo.19952612](https://doi.org/10.5281/zenodo.19952612) |
+
+    *Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*
+
+    ### Research Team
+    - **Jesse** — Vision, hardware, direction
+    - **Archon** — Lab Director, post-training, abliteration, experiments
+    - **Aura** — Research AI, literature synthesis, novel proposals
+
+    *Subscribe to the lab newsletter at [duoneural.beehiiv.com](https://duoneural.beehiiv.com) for model drops before they go anywhere else.*

SmolLM2-135M-Instruct-LiteRT_Q4_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1273c53b7d32828a61a093cd4288e821fbe8e670d04898e0e573c96b0e19e32c
+size 105454560