Instructions to use alexsofonea/Qwen3.5_2B_MLX_Nutrition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexsofonea/Qwen3.5_2B_MLX_Nutrition with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("alexsofonea/Qwen3.5_2B_MLX_Nutrition")
config = load_config("alexsofonea/Qwen3.5_2B_MLX_Nutrition")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use alexsofonea/Qwen3.5_2B_MLX_Nutrition with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "alexsofonea/Qwen3.5_2B_MLX_Nutrition"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "alexsofonea/Qwen3.5_2B_MLX_Nutrition"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use alexsofonea/Qwen3.5_2B_MLX_Nutrition 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 "alexsofonea/Qwen3.5_2B_MLX_Nutrition"

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 alexsofonea/Qwen3.5_2B_MLX_Nutrition

Run Hermes

hermes

OpenClaw new

How to use alexsofonea/Qwen3.5_2B_MLX_Nutrition with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "alexsofonea/Qwen3.5_2B_MLX_Nutrition"

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 "alexsofonea/Qwen3.5_2B_MLX_Nutrition" \
  --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"

Qwen3.5 Nutrition Finte Tune

This is a highly specialized, local-first Vision-Language Model (VLM) designed to estimate nutritional macros and physical weight from images of food. The model is fine-tuned to output strict, minified JSON payloads containing calories, macros (protein, fat, carbohydrates), and estimated dry/physical weight in grams.

It has been natively converted to Apple's MLX format and quantized to 4-bit (INT4), reducing its memory footprint to roughly ~1.5GB. This allows for lightning-fast, entirely offline, on-device inference on Apple Silicon hardware (macOS, iOS, iPadOS) using the Neural Engine.

Base Architecture: Qwen3.5-2B (Omni/Unified Vision-Language architecture)
Framework: MLX (mlx-vlm)
Quantization: INT4 (4-bit)
Fine-Tuning Method: LoRA (via Unsloth), merged to 16-bit prior to MLX conversion.
Primary Task: Multimodal Image-to-JSON parsing.

Intended Use

Primary Use Case: Seamless integration into native Swift/Apple ecosystem applications requiring on-device visual food analysis without relying on cloud APIs.
Input: A user prompt (e.g. food description) + a standard RGB food image.
Output: A standardized JSON schema containing absolute nutritional values.

Training Data & Credit

This model was fine-tuned on a custom dataset synthesized from a massive corpus of recipe data and user-uploaded images.

Dataset Source: Modified from the Food.com Recipes and Reviews dataset. The text macros were mathematically processed to derive estimated physical weights, combined with dynamically downloaded web images, and formatted into an OpenAI-style JSONL conversation structure.
Dataset License: Public Domain (CC0)

Licensing & Attribution

Model License: Apache License 2.0 (Inherited from the Qwen 3.5 2B base model).
Attribution: All base architectural credit belongs to the Qwen team at Alibaba Cloud.

Usage Example (MLX)

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import get_message_profile
from mlx_vlm.utils import load_image

# Load the INT4 MLX model
model, processor = load("alexsofonea/Qwen3.5_2B_MLX_Nutrition")

# Format input
image = load_image("path/to/food.jpg")
prompt = "Output JSON.\nEstimate."

# Generate JSON
output = generate(model, processor, prompt, image, max_tokens=150, temperature=0.0)
print(output)

About Developer

{
  "developer": {
    "name": "Alex",
    "age": 19,
    "location": "Munchen, Germany",
    "company": "Tecky",
    "role": "Founder",
    "background": [
      "Developer since age 7",
      "Filmmaker"
    ]
  },
  "mission": "Building local-first AI agents and integrating modern cinematic design into native software."
}

Contact:

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Safetensors

Model size

0.3B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for alexsofonea/Qwen3.5_2B_MLX_Nutrition

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Quantized

(141)

this model