Instructions to use MLA299/Tennda-Reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MLA299/Tennda-Reason with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("MLA299/Tennda-Reason")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use MLA299/Tennda-Reason with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MLA299/Tennda-Reason"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MLA299/Tennda-Reason"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use MLA299/Tennda-Reason with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "MLA299/Tennda-Reason"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "MLA299/Tennda-Reason"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "MLA299/Tennda-Reason",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use MLA299/Tennda-Reason with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MLA299/Tennda-Reason"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default MLA299/Tennda-Reason

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MLA299/Tennda-Reason with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MLA299/Tennda-Reason"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "MLA299/Tennda-Reason" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Tennda-Reason / README.md

MLA299

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metadata

library_name: mlx
pipeline_tag: text-generation
license: apache-2.0
tags:
  - mlx
  - lora
  - code
  - reasoning
  - text-generation
  - tennda
  - distillation

Tennda-Reason

A high-efficiency code & reasoning assistant fine-tuned by the Tennda Team Structured <think> reasoning followed by complete, production-ready answers — refined thinking, reliable delivery, built for code generation, math, and logical reasoning on Apple Silicon.

Model Overview

Item	Details
Model Name	Tennda-Reason
Developer	Tennda Team
Parameters	8.2B (4-bit quantized, ~4.3GB)
Architecture	Standard Transformer decoder, native `<think>` reasoning support
Weight Format	4-bit, MLX native
Framework	MLX 0.32.1 + mlx-lm 0.31.3 (Apple Silicon Metal acceleration)
Training	QLoRA (rank=16, scale=32, last 16 layers, 19.4M trainable params / 0.237%)
Training Data	Multi-teacher distillation SFT corpus (2,000 curated samples): math 27% · code 27% · reasoning 20% · instruction 14%
Context Length	1024 (trained), extensible via base capabilities
Release Date	2026-08-24

Highlights

Refined thinking: <think> reasoning chains compressed to 300–550 token key-point style — no rambling, no wasted tokens
Complete delivery: trained on "short thinking + complete answer" patterns; 0/5 test failures from runaway reasoning (baseline: 2/5)
Multi-domain: balanced across math, code, logical reasoning, and instruction following
Apple Silicon native: MLX 4-bit, ~6GB peak inference memory, runs on a single M-series machine

Training Details

Convergence (Loss)

Metric	Start	Best	Final
Train loss	1.70	—	0.421
Val loss	1.702	0.590 (iter 700)	0.794

2,000 iterations ≈ 2 epochs (batch=2, seq=1024, ~67 tokens/s, ~8h on a single M4)
GPU peak memory 8.5GB, stable throughout, zero OOM
Released checkpoint: iter 700 (best validation loss), selected via blind A/B output comparison against the final checkpoint

Checkpoint Selection

	iter 700 (released)	iter 2000
Val loss	0.590	0.794
Factual accuracy (networking task)	✅ correct	❌ detail error
Format compliance	✅ verified	✅

Evaluation (5 prompts, temp=0.3, vs pre-training baseline)

Task	Tennda-Reason	Baseline
Python quicksort	✅ complete runnable code + complexity analysis	❌ reasoning runaway, no answer produced
JS closures	✅ full structured tutorial	⚠️ thin output
SQL top salary per dept	✅ window-function solutions	❌ reasoning runaway, no answer produced
TCP 3-way handshake	✅ vivid analogy, correct steps	✅ concise & correct
Python HTTP server	⚠️ multi-approach, minor rough edges	✅ concise & correct

Summary: format compliance 5/5; runaway-reasoning failures reduced from 2/5 (baseline) to 0/5; overall usability substantially improved.

Usage (MLX)

from mlx_lm import load, generate

model, tokenizer = load("MLA299/Tennda-Reason")
messages = [{"role": "user", "content": "Write a quicksort in Python"}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048, verbose=False)
print(response)

Command line:

mlx_lm.generate --model MLA299/Tennda-Reason \
    --prompt "Write a SQL query: highest salary per department" \
    --max-tokens 2048

Recommended sampling: temp=0.3–0.7, max_tokens ≥ 2048 (thinking chain + full answer)

Limitations

Training data is English-dominant; Chinese works but is not specifically optimized
Post-SFT the model is more confident; factual-detail hallucinations are slightly higher than baseline — verify critical details in production
Trained at 1024 context; longer inputs rely on native capabilities
Contains synthetic distillation content; upstream data terms apply

License

Apache-2.0. See the license terms for redistribution conditions.

Citation

@misc{tennda-reason-2026,
  title = {Tennda-Reason: A Distillation-Fine-tuned Model for Code and Reasoning on Apple Silicon},
  author = {Tennda Team},
  year = {2026},
  month = {August},
  publisher = {Hugging Face},
  url = {https://huggingface.co/MLA299/Tennda-Reason}
}