---
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*