Tennda-Nano

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.

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Model Overview

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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

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Quality Metrics

Training Convergence (Loss)

• Stable convergence throughout training, no signs of overfitting (val/train gap remains steady)

Evaluation Scores (3 unseen samples, temp=0.3)

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.

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Inference Performance (measured on Apple M4)

Total latency reduced by ~83% on the same task compared with the baseline (output size reduced by 75%).

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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.

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Usage (MLX)

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:

mlx_lm.server --model MLA299/Tennda-Nano --port 8080

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Deployment Requirements

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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

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Tennda-Nano · In-house model · © 2026 Tennda Team