| --- |
| library_name: mlx |
| pipeline_tag: text-generation |
| license: other |
| tags: |
| - mlx |
| - code |
| - sql |
| - text-generation |
| - llm |
| - tennda |
| --- |
| |
| # Tennda-Nano |
|
|
| > **A lightweight code LLM independently developed by the Tennda team** |
| > Focused on **efficient, accurate** code generation and instruction following — direct answers, no redundant reasoning. |
|
|
| --- |
|
|
| ## Model Overview |
|
|
| | Item | Details | |
| |---|---| |
| | **Model Name** | Tennda-Nano | |
| | **Developer** | Tennda Team (in-house) | |
| | **Parameters** | Lightweight (<5B) | |
| | **Architecture** | Self-developed Transformer decoder (multimodal input) | |
| | **Weight Format** | bf16, MLX native | |
| | **Framework** | MLX (Apple Silicon Metal GPU acceleration) | |
| | **Training Data** | In-house code instruction dataset (20,022 samples: SQL / Python / algorithms, etc.) | |
| | **Release Date** | 2026-08-21 | |
|
|
| --- |
|
|
| ## Highlights |
|
|
| - **Direct answers**: instruction → immediate code/answer, extremely fast, **short total inference time** |
| - **High SQL accuracy**: 100% correct on unseen SQL tasks |
| - **Clean output**: concise, no verbose reasoning, production-ready answers |
| - **Deployment friendly**: lightweight single-machine inference, low resource usage, suitable for edge/personal devices |
|
|
| --- |
|
|
| ## Quality Metrics |
|
|
| ### Training Convergence (Loss) |
|
|
| | Metric | Start | Final | Reduction | |
| |---|---|---|---| |
| | Train loss | 1.929 | **0.963** | -50% | |
| | Val loss | 4.500 | **0.844** | **-81%** | |
|
|
| - Stable convergence throughout training, no signs of overfitting (val/train gap remains steady) |
|
|
| ### Evaluation Scores (3 unseen samples, temp=0.3) |
|
|
| | # | Task Type | Result | |
| |---|---|---| |
| | 1 | SQL conditional query (age>45) | ✅ Directly outputs `SELECT * FROM employees WHERE age > 45;` | |
| | 2 | Design a Car class (Python) | ⚠️ Occasional loop repetition (mitigable via sampling params) | |
| | 3 | SQL aggregate query (MAX salary) | ✅ Directly outputs `SELECT MAX(SALARY) FROM EMPLOYEE;` | |
|
|
| **SQL accuracy: 100% (2/2)** |
| **Format alignment (direct answer, no redundant reasoning): 67% (2/3)** |
|
|
| > Note: current evaluation is a sampled manual assessment; scores are for reference only. A full evaluation pipeline is under continuous development. |
|
|
| --- |
|
|
| ## Inference Performance (measured on Apple M4) |
|
|
| | Metric | Result | |
| |---|---| |
| | Model load | ~4s | |
| | Single-task generation | **~3.4s** | |
| | Output style | Direct answer (no verbose thinking) | |
|
|
| > Total latency reduced by **~83%** on the same task compared with the baseline (output size reduced by 75%). |
|
|
| --- |
|
|
| ## Training Method |
|
|
| - **Architecture**: self-developed Transformer decoder (35 layers, multimodal encoding) |
| - **Method**: in-house lightweight training pipeline (parameter-efficient training, fully local) |
| - **Cost**: 26 minutes on a single machine, peak memory 14.4GB, no large-scale compute cluster required |
| - **Artifact**: complete inference weights (MLX format) |
|
|
| > Details in the training report and comparison report. |
|
|
| --- |
|
|
| ## Usage (MLX) |
|
|
| ```python |
| from mlx_lm import load, generate |
| from mlx_lm.sample_utils import make_sampler |
| |
| model, tokenizer = load("MLA299/Tennda-Nano") |
| sampler = make_sampler(temp=0.3, top_p=0.9) |
| prompt = tokenizer.apply_chat_template( |
| [{"role": "user", "content": "Write a SQL query to select employees older than 45."}], |
| add_generation_prompt=True, |
| ) |
| print(generate(model, tokenizer, prompt=prompt, max_tokens=256, sampler=sampler)) |
| ``` |
|
|
| Or start an OpenAI-compatible API server: |
|
|
| ```bash |
| mlx_lm.server --model MLA299/Tennda-Nano --port 8080 |
| ``` |
|
|
| --- |
|
|
| ## Deployment Requirements |
|
|
| | Config | Requirement | |
| |---|---| |
| | Memory | ≥ 16GB (24GB recommended) | |
| | Platform | Apple Silicon (M-series, Metal GPU) | |
| | Inference | mlx-lm ≥ 0.30 | |
| | Quantization | Can be 4-bit quantized for 8GB-memory devices | |
|
|
| --- |
|
|
| ## Known Limitations |
|
|
| - Chinese instruction capability is still under optimization (training corpus is primarily English code instructions) |
| - Occasional loop repetition (can be mitigated with `repetition_penalty` at inference time) |
| - The concise answer style may omit necessary explanations |
|
|
| --- |
|
|
| *Tennda-Nano · In-house model · © 2026 Tennda Team* |
|
|
