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Model: Swicked86/phi4-mm-gguf
Source: Original Platform

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+phi4-mm-f16.gguf filter=lfs diff=lfs merge=lfs -text
+phi4-mm-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+phi4-mm-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+mmproj-phi4-mm-f16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+base_model: microsoft/Phi-4-multimodal-instruct
+tags:
+  - phi4
+  - phi4mm
+  - gguf
+  - quantized
+  - vision-language
+  - audio
+  - multimodal
+  - llama-cpp
+  - lm-studio
+language:
+  - en
+pipeline_tag: visual-question-answering
+library_name: gguf
+---
+
+# Phi-4-multimodal-instruct GGUF Quantizations
+
+GGUF quantizations of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) —
+a **5.6 B parameter** multimodal model by Microsoft supporting **text, vision (images), and audio** inputs.
+
+Produced with [llama.cpp](https://github.com/ggerganov/llama.cpp) build **b8347** on RTX 5090.
+
+> **License:** [MIT](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/LICENSE) — © Microsoft Corporation.
+> These quantizations carry the same MIT license as the original model.
+
+---
+
+## Available Files
+
+| File | Quant | Size | BPW | Best for |
+|------|-------|------|-----|----------|
+| `phi4-mm-f16.gguf` | F16 | 7.17 GB | 16.0 | Re-quantization base, maximum quality |
+| `phi4-mm-Q8_0.gguf` | Q8_0 | 3.90 GB | 8.0 | High-end GPU — near-lossless |
+| `phi4-mm-Q4_K_M.gguf` | Q4_K_M | 2.37 GB | 5.18 | CPU / constrained VRAM — good quality |
+| `mmproj-phi4-mm-f16.gguf` | F16 | 825 MB | 16.0 | Vision encoder — **required for image input** |
+
+> **One mmproj for all:** `mmproj-phi4-mm-f16.gguf` works with every text GGUF above.
+> It cannot be quantized further — the CLIP FF layer dimension (4304) is not divisible by 32.
+
+---
+
+## VRAM Requirements
+
+Full GPU offload (`-ngl 99`):
+
+| Configuration | VRAM |
+|---------------|------|
+| F16 + mmproj-F16 | ~10,000 MiB |
+| Q8_0 + mmproj-F16 | ~5,400 MiB |
+| Q4_K_M + mmproj-F16 | ~3,500 MiB |
+
+---
+
+## Quality Metrics
+
+### Perplexity (wikitext-2-raw test set, context 512)
+
+| Model | PPL | vs F16 |
+|-------|-----|--------|
+| F16 (baseline) | 14.9338 ± 0.107 | — |
+| Q8_0 | 14.9107 ± 0.106 | −0.15% ✅ lossless |
+| Q4_K_M | 16.3183 ± 0.121 | +9.3% |
+
+### Throughput (llama-bench, RTX 5090, pp512 / tg128)
+
+| Model | Prompt (t/s) | Generation (t/s) |
+|-------|-------------|-----------------|
+| Q8_0 | 21,352 | 247 |
+| Q4_K_M | 19,904 | 324 |
+
+### Multimodal Benchmarks (lmms-eval)
+
+*VQA evaluation in progress — will be added when complete. Suite: MMStar, OCRBench, AI2D, MathVista, HallusionBench.*
+
+---
+
+## Usage
+
+### LM Studio (Recommended for Desktop)
+
+> **LM Studio** has native GGUF support including multimodal vision. No command-line needed.
+
+**Text-only:**
+1. Open LM Studio → search `Swicked86/phi4-mm-gguf`
+2. Download `phi4-mm-Q8_0.gguf` (GPU) or `phi4-mm-Q4_K_M.gguf` (CPU / low VRAM)
+3. Load the model → Chat
+
+**With vision (image input):**
+1. Download both `phi4-mm-Q8_0.gguf` **and** `mmproj-phi4-mm-f16.gguf`
+2. Load the main model in LM Studio
+3. In **Model Settings → Multimodal → Vision Model (mmproj)**, browse to `mmproj-phi4-mm-f16.gguf`
+4. In Chat, click the image icon to attach a photo and ask questions about it
+
+> The mmproj file is the vision encoder. Without it the model runs text-only.  
+> `mmproj-phi4-mm-f16.gguf` is compatible with **all three** text GGUFs.
+
+---
+
+### llama.cpp CLI
+
+#### Step 1 — Download the files
+
+```bash
+# Install huggingface-cli if needed
+pip install huggingface_hub
+
+# Text + vision (recommended)
+huggingface-cli download Swicked86/phi4-mm-gguf phi4-mm-Q8_0.gguf mmproj-phi4-mm-f16.gguf --local-dir ./phi4-mm
+
+# CPU / low-VRAM variant
+huggingface-cli download Swicked86/phi4-mm-gguf phi4-mm-Q4_K_M.gguf mmproj-phi4-mm-f16.gguf --local-dir ./phi4-mm
+```
+
+Build llama.cpp if you haven't already:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+cmake -B build -DGGML_CUDA=ON    # omit DGGML_CUDA=ON for CPU-only
+cmake --build build --config Release -j$(nproc)
+```
+
+---
+
+#### Step 2 — Interactive multimodal chat (images + text)
+
+`llama-mtmd-cli` launches an interactive session. Type your prompt, or prefix it with an image path using `/image`:
+
+```bash
+./build/bin/llama-mtmd-cli \
+  -m ./phi4-mm/phi4-mm-Q8_0.gguf \
+  --mmproj ./phi4-mm/mmproj-phi4-mm-f16.gguf \
+  -ngl 99 --threads 16 --ctx-size 8192
+```
+
+Inside the session:
+```
+> /image photo.jpg
+Image loaded.
+> What is in this image?
+[model describes the image]
+
+> /image chart.png
+Image loaded.
+> Summarise the trend shown in this chart.
+[model analyses the chart]
+
+> Explain the previous image again but in French.
+[responds without re-loading the image]
+```
+
+Single-shot (non-interactive):
+```bash
+./build/bin/llama-mtmd-cli \
+  -m ./phi4-mm/phi4-mm-Q8_0.gguf \
+  --mmproj ./phi4-mm/mmproj-phi4-mm-f16.gguf \
+  --image photo.jpg \
+  -p "Describe this image in detail." \
+  -ngl 99 --threads 16 --no-display-prompt
+```
+
+CPU (no GPU):
+```bash
+./build/bin/llama-mtmd-cli \
+  -m ./phi4-mm/phi4-mm-Q4_K_M.gguf \
+  --mmproj ./phi4-mm/mmproj-phi4-mm-f16.gguf \
+  --image photo.jpg \
+  -p "What objects are in this photo?" \
+  --threads 8
+```
+
+---
+
+#### Text-only (no image)
+
+```bash
+./build/bin/llama-cli \
+  -m ./phi4-mm/phi4-mm-Q8_0.gguf \
+  --ctx-size 65536 --flash-attn on --kv-offload -ngl 99 --threads 16 \
+  -p "<|system|>You are a helpful assistant.<|end|><|user|>Hello<|end|><|assistant|>"
+```
+
+---
+
+### llama-server — OpenAI-compatible API
+
+Serves both text and vision via `/v1/chat/completions`. Useful for integrations (Open WebUI, SillyTavern, Continue.dev, etc.):
+
+```bash
+./build/bin/llama-server \
+  -m ./phi4-mm/phi4-mm-Q8_0.gguf \
+  --mmproj ./phi4-mm/mmproj-phi4-mm-f16.gguf \
+  -ngl 99 --threads 16 \
+  --ctx-size 8192 --parallel 4 \
+  --port 8080 --host 127.0.0.1
+```
+
+**Text query (curl):**
+```bash
+curl http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "phi4-mm",
+    "messages": [{"role": "user", "content": "Explain the Pythagorean theorem."}],
+    "max_tokens": 300
+  }'
+```
+
+**Image query (curl, base64):**
+```bash
+IMAGE_B64=$(base64 -w0 photo.jpg)
+curl http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"model\": \"phi4-mm\",
+    \"messages\": [{
+      \"role\": \"user\",
+      \"content\": [
+        {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/jpeg;base64,${IMAGE_B64}\"}},
+        {\"type\": \"text\", \"text\": \"What is in this image?\"}
+      ]
+    }],
+    \"max_tokens\": 300
+  }"
+```
+
+**Image query (Python, openai SDK):**
+```python
+import base64, httpx
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="local")
+
+with open("photo.jpg", "rb") as f:
+    image_b64 = base64.b64encode(f.read()).decode()
+
+response = client.chat.completions.create(
+    model="phi4-mm",
+    messages=[{
+        "role": "user",
+        "content": [
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }],
+    max_tokens=300,
+)
+print(response.choices[0].message.content)
+```
+
+#### Modality support summary for GGUF (llama.cpp / LM Studio)
+
+| Modality | Supported | Notes |
+|----------|-----------|-------|
+| Text | ✅ | All three GGUFs |
+| Vision (images) | ✅ | Requires `mmproj-phi4-mm-f16.gguf` + `--mmproj` flag |
+| Audio / Speech | ❌ | Not available — see below |
+
+> **Audio is not supported in the GGUF files.** phi4-mm's speech capability uses a custom conformer-based audio encoder (24 conformer blocks, initialized from a proprietary AED ASR model) plus a rank-320 speech LoRA applied to the language decoder. The GGUF conversion pipeline (`convert_hf_to_gguf.py`) only exports the text transformer and the SigLIP vision encoder (mmproj) — the audio encoder tensors are not extracted. There is currently no `audioproj` equivalent in llama.cpp for phi4-mm.
+>
+> **To use audio/speech transcription, use the vLLM path below** with the original bf16 safetensors model.
+
+---
+
+### vLLM — Full Multimodal (Image + Audio, bf16 Safetensors)
+
+> **Use vLLM when you want maximum quality and full multimodal support** (images + audio) from the original bf16 safetensors weights. For constrained hardware, use the GGUF options above instead.
+
+**Requirements:** Python 3.10+, CUDA GPU with ~16 GB VRAM, vLLM 0.7.0+
+
+#### 1 — Install vLLM and download the model
+
+```bash
+python3 -m venv ~/.vllm-env
+source ~/.vllm-env/bin/activate
+pip install --upgrade pip
+pip install vllm
+
+# Download the original safetensors model (~14 GB, 3 shards)
+huggingface-cli login          # paste your HF token if the model is gated
+huggingface-cli download microsoft/Phi-4-multimodal-instruct \
+    --local-dir ~/phi4-mm-hf
+```
+
+#### 2 — Launch the vLLM server
+
+```bash
+source ~/.vllm-env/bin/activate
+python -m vllm.entrypoints.openai.api_server \
+  --model ~/phi4-mm-hf \
+  --dtype bfloat16 \
+  --trust-remote-code \
+  --max-model-len 65520 \
+  --kv-cache-memory-bytes 8G \
+  --limit-mm-per-prompt '{"image": 3, "audio": 3}' \
+  --enable-auto-tool-choice \
+  --tool-call-parser phi4_mini_json \
+  --port 8080 \
+  --host 127.0.0.1 \
+  --served-model-name phi4-mm
+```
+
+| Flag | Value | Notes |
+|------|-------|-------|
+| `--dtype bfloat16` | — | Native dtype — do not change to float16 |
+| `--max-model-len 65520` | — | Stable context ceiling for phi4-mm (131 K nominal) |
+| `--kv-cache-memory-bytes` | `8G` | Tune down to `4G` on 12–16 GB GPUs |
+| `--limit-mm-per-prompt` | `{"image":3,"audio":3}` | Max attachments per request |
+| `--tool-call-parser phi4_mini_json` | — | phi4-mm emits `functools[...]` not Hermes — required for tool calling |
+| `--trust-remote-code` | — | Required for phi4-mm's custom modelling code |
+
+> **Official vLLM LoRA flags:** Microsoft's published vLLM command includes explicit LoRA adapter flags to activate
+> the rank-320 vision and speech adapters stored in separate subfolders of the model directory:
+> ```bash
+> --enable-lora \
+> --max-lora-rank 320 \
+> --lora-extra-vocab-size 0 \
+> --max-loras 2 \
+> --lora-modules speech=~/phi4-mm-hf/speech-lora vision=~/phi4-mm-hf/vision-lora
+> ```
+> If you experience degraded vision or audio quality, add these flags to the launch command above.
+
+Wait for the server to finish loading (~60 s):
+```bash
+curl http://localhost:8080/health   # → {"status":"ok"}
+```
+
+#### 3 — Send requests (OpenAI-compatible API)
+
+**Text (Python):**
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="local")
+response = client.chat.completions.create(
+    model="phi4-mm",
+    messages=[{"role": "user", "content": "What is the capital of France?"}],
+    max_tokens=256,
+)
+print(response.choices[0].message.content)
+```
+
+**Image (Python):**
+```python
+import base64
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="local")
+
+with open("photo.jpg", "rb") as f:
+    image_b64 = base64.b64encode(f.read()).decode()
+
+response = client.chat.completions.create(
+    model="phi4-mm",
+    messages=[{
+        "role": "user",
+        "content": [
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
+            {"type": "text", "text": "Describe this image in detail."},
+        ],
+    }],
+    max_tokens=512,
+)
+print(response.choices[0].message.content)
+```
+
+**Image (curl, base64):**
+```bash
+IMAGE_B64=$(base64 -w0 photo.jpg)
+curl http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"model\": \"phi4-mm\",
+    \"messages\": [{
+      \"role\": \"user\",
+      \"content\": [
+        {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/jpeg;base64,${IMAGE_B64}\"}},
+        {\"type\": \"text\", \"text\": \"What is in this image?\"}
+      ]
+    }],
+    \"max_tokens\": 300
+  }"
+```
+
+**Audio — speech transcription / understanding (Python):**
+```python
+import base64
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="local")
+
+with open("audio.wav", "rb") as f:
+    audio_b64 = base64.b64encode(f.read()).decode()
+
+response = client.chat.completions.create(
+    model="phi4-mm",
+    messages=[{
+        "role": "user",
+        "content": [
+            {"type": "input_audio", "input_audio": {"data": audio_b64, "format": "wav"}},
+            {"type": "text", "text": "Transcribe this audio."},
+        ],
+    }],
+    max_tokens=512,
+)
+print(response.choices[0].message.content)
+```
+
+> phi4-mm uses a custom conformer-based audio encoder with rank-320 speech LoRA — no separate ASR model needed.
+> Supported formats: `wav`, `mp3`, `ogg`, `flac`.
+
+#### Tool calling
+
+phi4-mm emits tool calls as `functools[{"name":"...","arguments":{...}}]`.
+The `--tool-call-parser phi4_mini_json` flag (vLLM 0.7+) handles this format automatically.
+For a complete chat template that injects tools into phi4-mm's native `<|tool|>...<|/tool|>` block,
+see [`deploy/wsl-vllm/phi4-mm-tool-template.jinja`](deploy/wsl-vllm/phi4-mm-tool-template.jinja)
+in the [companion repo](https://github.com/Swicked86/LLM_Creation).
+
+---
+
+### Ollama (CPU / NUC / edge)
+
+See the [`deploy/`](deploy/) folder for a complete `Modelfile`, NUC install script, and OpenClaw integration config.
+
+```
+FROM ./phi4-mm-Q4_K_M.gguf
+PARAMETER num_ctx 8192
+PARAMETER num_thread 8
+PARAMETER num_gpu 0
+PARAMETER flash_attn false
+PARAMETER temperature 0.7
+```
+
+---
+
+## Architecture
+
+| Property | Value |
+|----------|-------|
+| Base model | Phi-4-Mini (3.8 B LLM backbone) |
+| Total parameters | ~5.6 B |
+| GGUF arch | `phi3` |
+| Context length | 128 K tokens (131,072) |
+| Modalities | Text, Vision (SigLIP-400M), Audio/Speech |
+
+The vision encoder (`mmproj-phi4-mm-f16.gguf`) is a SigLIP-400M encoder finetuned
+with LLM2CLIP, with a 2-layer MLP projector. Audio/speech is **not embedded in the GGUF** — see the audio limitation callout above.
+
+---
+
+## Conversion Details
+
+| Item | Value |
+|------|-------|
+| Converter | `llama.cpp convert_hf_to_gguf.py` (text) + custom mmproj converter |
+| llama.cpp build | b8347 / `fc350fdf9` |
+| Source | [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) |
+| Hardware | RTX 5090 32 GB, CUDA 12.0, WSL2 Ubuntu 24.04 |
+
+Quantization commands:
+
+```bash
+# Q8_0
+llama-quantize phi4-mm-f16.gguf phi4-mm-Q8_0.gguf Q8_0
+
+# Q4_K_M
+llama-quantize phi4-mm-f16.gguf phi4-mm-Q4_K_M.gguf Q4_K_M
+```
+
+---
+
+## NUC / Edge Deployment
+
+The `Q4_K_M + mmproj-phi4-mm-f16.gguf` combination (~3,500 MiB VRAM) fits on:
+
+- Intel NUC 13/14 Pro (Intel Arc iGPU, 4–8 GB shared VRAM)
+- Systems with 8 GB unified memory (Apple Silicon M-series, etc.)
+
+See [`deploy/`](deploy/) for install scripts and configuration.
+
+---
+
+## Related
+
+- [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) — original model weights
+- [llama.cpp](https://github.com/ggerganov/llama.cpp) — inference engine
+- [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) — multimodal evaluation harness

deploy/Modelfile

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+# Ollama Modelfile — Phi-4 Multimodal Instruct Q4_K_M
+# Optimised for: Intel 11th Gen NUC, 8 GB RAM, CPU-only
+#
+# Source model : microsoft/Phi-4-multimodal-instruct
+# License      : MIT  https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/LICENSE
+# Quantization : Q4_K_M via llama.cpp llama-quantize
+# Architecture : phi3 (3.8B LLM backbone + vision/speech adapters in base GGUF)
+
+FROM ./phi4-mm-Q4_K_M.gguf
+
+# ── Context & KV cache ───────────────────────────────────────────────────────
+# 8 192 tokens balances capability vs RAM on 8 GB hardware.
+# Lower to 4096 if you observe OOM / heavy swapping.
+PARAMETER num_ctx 8192
+
+# ── CPU tuning ───────────────────────────────────────────────────────────────
+# 11th Gen NUC typically 4 cores / 8 logical threads (i5/i7-1135G7 / 1165G7).
+# Reduce to 4 if the NUC is a Core i3 variant.
+PARAMETER num_thread 8
+
+# No discrete GPU — all layers run on CPU.
+PARAMETER num_gpu 0
+
+# Flash attention is a GPU feature; disable for CPU inference.
+PARAMETER flash_attn false
+
+# ── Generation defaults ───────────────────────────────────────────────────────
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER repeat_penalty 1.1
+PARAMETER stop "<|end|>"
+PARAMETER stop "<|user|>"
+PARAMETER stop "<|assistant|>"
+
+# ── System prompt ─────────────────────────────────────────────────────────────
+SYSTEM """You are a helpful, accurate, and concise AI assistant. You excel at reasoning, analysis, writing, coding, and answering questions. Be direct and thorough."""

deploy/README-deployment.md

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+---
+license: mit
+base_model: microsoft/Phi-4-multimodal-instruct
+tags:
+  - phi4mm
+  - gguf
+  - quantized
+  - q4_k_m
+  - cpu
+  - ollama
+  - llama-cpp
+language:
+  - multilingual
+  - ar
+  - zh
+  - cs
+  - da
+  - nl
+  - en
+  - fi
+  - fr
+  - de
+  - he
+  - hu
+  - it
+  - ja
+  - ko
+  - "no"
+  - pl
+  - pt
+  - ru
+  - es
+  - sv
+  - th
+  - tr
+  - uk
+pipeline_tag: text-generation
+---
+
+# Phi-4 Multimodal Instruct — GGUF Quantizations
+
+CPU-optimised GGUF quantizations of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) produced with [llama.cpp](https://github.com/ggerganov/llama.cpp).
+
+## Model Summary
+
+Phi-4-multimodal-instruct is a **5.6 B parameter** lightweight multimodal foundation model by Microsoft that processes text, image, and audio inputs. The backbone LLM is **Phi-4-Mini** (3.8 B). This repository contains GGUF quantizations for local CPU and GPU deployment.
+
+- **Context length:** 128 K tokens (131 072)
+- **Architecture:** `phi3` (GGUF)  
+- **License:** [MIT](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/LICENSE) — © Microsoft Corporation
+
+## Available Files
+
+| File | Quant | Size | BPW | Best for |
+|------|-------|------|-----|----------|
+| `phi4-mm-Q4_K_M.gguf` | Q4_K_M | 2.37 GB | 5.18 | CPU inference, 8 GB RAM systems |
+| `phi4-mm-Q8_0.gguf`   | Q8_0   | 3.90 GB | 8.00 | GPU/high-RAM systems, near-lossless |
+| `phi4-mm-f16.gguf`    | F16    | 7.17 GB  | 16.00 | Source / re-quantization base |
+
+## Quantization Method
+
+Quantized using `llama-quantize` from [ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) (build 8334).
+
+```bash
+llama-quantize phi4-mm-f16.gguf phi4-mm-Q4_K_M.gguf Q4_K_M 16
+```
+
+Source weights converted from the original safetensors using `convert_hf_to_gguf.py`.
+
+## Recommended Usage
+
+### Ollama (CPU — Intel NUC / low-power hardware)
+
+```bash
+# Pull and run (once uploaded to Ollama registry)
+ollama run phi4-mm-nuc
+
+# Or import from GGUF directly with a Modelfile:
+ollama create phi4-mm-nuc -f Modelfile
+ollama run phi4-mm-nuc
+```
+
+**Modelfile for 8 GB RAM / no GPU:**
+
+```
+FROM phi4-mm-Q4_K_M.gguf
+PARAMETER num_ctx 8192
+PARAMETER num_thread 8
+PARAMETER num_gpu 0
+PARAMETER flash_attn false
+PARAMETER temperature 0.7
+PARAMETER repeat_penalty 1.1
+SYSTEM "You are a helpful, accurate, and concise AI assistant."
+```
+
+### llama.cpp CLI (GPU, full quality)
+
+```bash
+./build/bin/llama-cli \
+  -m phi4-mm-Q8_0.gguf \
+  --ctx-size 65536 \
+  --flash-attn on \
+  --kv-offload \
+  -ngl 99 \
+  --threads 16
+```
+
+### OpenClaw Integration
+
+In `~/.openclaw/openclaw.json`:
+
+```json
+{
+  "agent": {
+    "model": "ollama/phi4-mm-nuc"
+  },
+  "modelConfigs": {
+    "ollama/phi4-mm-nuc": {
+      "provider": "ollama",
+      "model": "phi4-mm-nuc",
+      "baseUrl": "http://localhost:11434"
+    }
+  }
+}
+```
+
+## Hardware Notes
+
+| Hardware | Recommended quant | Context | Notes |
+|---|---|---|---|
+| Intel NUC 11th Gen, 8 GB RAM | Q4_K_M | 8 192 | CPU-only, `num_gpu 0` |
+| Laptop / desktop, 16 GB RAM | Q5_K_M or Q8_0 | 16 384 | CPU or iGPU |
+| GPU with ≥ 8 GB VRAM | Q8_0 or F16 | 32 768–65 536 | Full `-ngl 99` offload |
+
+## License
+
+This repository redistributes quantized weights derived from [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) under the original **MIT License**.
+
+```
+MIT License
+Copyright (c) Microsoft Corporation.
+```
+
+Quantization tooling (llama.cpp) is also MIT licensed. See [llama.cpp LICENSE](https://github.com/ggerganov/llama.cpp/blob/master/LICENSE).
+
+## Attribution
+
+- Original model: Microsoft Research  
+- Quantization: produced with [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov et al.  
+- Technical report: [arXiv:2503.01743](https://arxiv.org/abs/2503.01743)

deploy/install-nuc.sh

@@ -0,0 +1,89 @@
+#!/usr/bin/env bash
+# =============================================================================
+# install-nuc.sh
+# Deploy Phi-4 Multimodal Q4_K_M to an Intel NUC (8 GB RAM, CPU-only)
+# and register it with Ollama for use with OpenClaw.
+#
+# Usage:
+#   On the NUC, run:
+#       bash install-nuc.sh [path/to/phi4-mm-Q4_K_M.gguf]
+#
+#   Or from the build machine, copy files first then run:
+#       scp phi4-mm-Q4_K_M.gguf Modelfile openclaw-config.json user@nuc-ip:~/phi4-deploy/
+#       ssh user@nuc-ip "cd ~/phi4-deploy && bash install-nuc.sh"
+# =============================================================================
+set -euo pipefail
+
+GGUF_DEFAULT="/home/$(whoami)/phi4-deploy/phi4-mm-Q4_K_M.gguf"
+GGUF_PATH="${1:-$GGUF_DEFAULT}"
+MODELFILE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MODEL_NAME="phi4-mm-nuc"
+
+echo "============================================================"
+echo " Phi-4 Multimodal NUC Deployment"
+echo "============================================================"
+echo " GGUF    : $GGUF_PATH"
+echo " Model   : $MODEL_NAME"
+echo " Host    : $(hostname)"
+echo "============================================================"
+
+# ── 1. Verify GGUF exists ─────────────────────────────────────────────────────
+if [[ ! -f "$GGUF_PATH" ]]; then
+  echo "ERROR: GGUF not found at $GGUF_PATH"
+  echo "Transfer it first:  scp <build-host>:~/models/phi4-mm-Q4_K_M.gguf $GGUF_PATH"
+  exit 1
+fi
+
+# ── 2. Install Ollama if missing ──────────────────────────────────────────────
+if ! command -v ollama &>/dev/null; then
+  echo ""
+  echo ">>> Installing Ollama..."
+  curl -fsSL https://ollama.com/install.sh | sh
+  echo ">>> Ollama installed."
+else
+  echo ">>> Ollama already installed: $(ollama --version)"
+fi
+
+# ── 3. Ensure Ollama daemon is running ────────────────────────────────────────
+if ! pgrep -x ollama &>/dev/null; then
+  echo ">>> Starting Ollama daemon..."
+  ollama serve &>/dev/null &
+  sleep 3
+fi
+
+# ── 4. Patch Modelfile with absolute GGUF path ───────────────────────────────
+MODELFILE_TMP="$(mktemp)"
+sed "s|FROM ./phi4-mm-Q4_K_M.gguf|FROM ${GGUF_PATH}|" \
+  "${MODELFILE_DIR}/Modelfile" > "$MODELFILE_TMP"
+
+# ── 5. Register model with Ollama ─────────────────────────────────────────────
+echo ""
+echo ">>> Creating Ollama model: $MODEL_NAME"
+ollama create "$MODEL_NAME" -f "$MODELFILE_TMP"
+rm "$MODELFILE_TMP"
+echo ">>> Model registered."
+
+# ── 6. Smoke-test ─────────────────────────────────────────────────────────────
+echo ""
+echo ">>> Running smoke test (one token)..."
+RESPONSE=$(ollama run "$MODEL_NAME" "Reply with exactly: OK" --nowordwrap 2>&1 | head -5)
+echo "    Response: $RESPONSE"
+
+# ── 7. Systemd user service for Ollama (optional) ─────────────────────────────
+if command -v systemctl &>/dev/null && systemctl --user status ollama.service &>/dev/null 2>&1; then
+  echo ">>> Ollama systemd service already configured."
+else
+  echo ""
+  echo ">>> To keep Ollama running at startup, enable the service:"
+  echo "    sudo systemctl enable --now ollama"
+fi
+
+# ── Done ──────────────────────────────────────────────────────────────────────
+echo ""
+echo "============================================================"
+echo " DONE. Model is ready."
+echo ""
+echo " Test:           ollama run $MODEL_NAME"
+echo " API endpoint:   http://localhost:11434/v1"
+echo " OpenClaw model: ollama/$MODEL_NAME"
+echo "============================================================"

deploy/openclaw-config.json

@@ -0,0 +1,12 @@
+{
+  "agent": {
+    "model": "ollama/phi4-mm-nuc"
+  },
+  "modelConfigs": {
+    "ollama/phi4-mm-nuc": {
+      "provider": "ollama",
+      "model": "phi4-mm-nuc",
+      "baseUrl": "http://localhost:11434"
+    }
+  }
+}

mmproj-phi4-mm-f16.gguf

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+oid sha256:d4a05e61d2485afd651d69bc2a69c1b79e06a6661689523c3a17124cf9cf40a3
+size 825297088

phi4-mm-Q4_K_M.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f6ce167990dc4b73dcff7af00c62630c20fb5d40035affdd8bb77ace195c726
+size 2493840768

phi4-mm-Q8_0.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06fe4c0f9537fcac17930a8f75d91eaf76419bc3b765ac203ca37da540f27ea9
+size 4084611456

phi4-mm-f16.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:548ec43dacb57b41cf742fdb0d893edfe6096706ccd372df4e41fa5dfe4f5d68
+size 7680694656