| --- |
| license: apache-2.0 |
| language: |
| - ar |
| pipeline_tag: text-generation |
| tags: |
| - gguf |
| - llama-cpp |
| - egyptian-arabic |
| - conversational |
| - cpu |
| base_model: Qwen/Qwen2.5-1.5B-Instruct |
| --- |
| |
| # Mini-NABD — lightweight Egyptian-Arabic chat model (GGUF) |
|
|
| Mini-NABD is a tiny conversational model that speaks Egyptian Arabic (Masri) |
| like a friend: casual, funny when natural, short answers. It is an **additional** |
| fast/local companion to the main NABD AI, not a replacement. |
|
|
| ## Files |
|
|
| | file | what | size | RAM | |
| |---|---|---|---| |
| | `mini-nabd-q4_k_m.gguf` | recommended 4-bit | ~1.0 GB | ~2 GB | |
|
|
| Only the smallest practical file is published — no variant zoo. |
|
|
| ## Run it (CPU, 4GB RAM, no GPU) |
|
|
| ```bash |
| # llama.cpp server (OpenAI-compatible) |
| ./llama-server -m mini-nabd-q4_k_m.gguf -c 2048 --port 11435 |
| |
| curl http://127.0.0.1:11435/v1/chat/completions -H 'Content-Type: application/json' -d '{ |
| "model": "mini-nabd", |
| "messages": [ |
| {"role": "system", "content": "أنت نبض الصغير — صاحب مصري خفيف الدم، رد بالعامية مختصر."}, |
| {"role": "user", "content": "عامل ايه؟"} |
| ]}' |
| ``` |
|
|
| Or Python: `pip install llama-cpp-python`, then `Llama("mini-nabd-q4_k_m.gguf", n_ctx=2048)`. |
|
|
| - Recommended context: **2048** (fits 4GB with room to spare). |
| - Startup: seconds. Speed on a normal laptop CPU: roughly 15–40 tok/s |
| (measured numbers go in the release notes per build). |
| - Temp 0.7, max ~256 tokens — it is tuned for short replies. |
|
|
| ## NABD integration + Tavily example |
|
|
| ```js |
| const { autoProvider, needsSearch } = require('./router'); // providers/router |
| const { tavilySearch, formatForMini } = require('./tavily'); // providers/tavily |
| |
| async function answer(userText) { |
| let context = ''; |
| if (needsSearch(userText)) { // router decides, not the tiny model |
| const t = await tavilySearch(userText); // TAVILY_API_KEY stays server-side |
| context = formatForMini(userText, t) + '\n'; |
| } |
| const sys = 'أنت نبض الصغير — صاحب مصري خفيف الدم، رد بالعامية مختصر.' |
| + (context ? '\n' + context : ''); |
| return miniReply(sys, userText); // POST to 127.0.0.1:11435, fallback main |
| } |
| ``` |
|
|
| ## Training (v0 status: base + system prompt) |
|
|
| v0 ships `Qwen2.5-1.5B-Instruct` Q4_K_M unchanged — the Egyptian personality |
| comes from the system prompt in `SYSTEM.md`, not from weights yet. Measured |
| gaps on the raw base (Arabizi loops, MSA-style refusals of light slang) are |
| documented and are exactly what v1 fixes. |
|
|
| v1 roadmap (ready to run, see `training/`): LoRA r=16 on 60 hand-written |
| Egyptian pairs (greetings, slang, Arabizi, humor, live-info refusals) on a |
| Colab T4, merge + Q4_K_M export replacing this file. Tool routing stays |
| external, so weights will contain zero tool tokens. |
|
|
| ## Limitations |
|
|
| - Small talk + simple explanations only — hard reasoning, long code, and |
| vision go to the main AI. |
| - Knowledge cutoff = base model; live facts need Tavily via the router. |
| - Egyptian dialect is a style layer over Qwen's Arabic — MSA slips through |
| on technical topics. |
|
|
| ## License |
|
|
| Apache-2.0 (inherits Qwen2.5 + Llama.cpp ecosystem licensing for the base). |
|
|
