nutribrain-mistral7b-merged

NutriBrain — Mistral-7B (Merged)

NutriBrain is a fine-tuned Mistral-7B model designed to convert natural language meal logging into structured JSON output.

The model is optimized for:

• Meal logging
• Nutrition tracking
• Structured intent extraction
• JSON-only responses

It is trained to output clean machine-readable JSON for downstream nutrition systems.

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Model Details

• Developed by: Shreyas Jani
• Model type: Causal Language Model (Decoder-only Transformer)
• Base model: mistralai/Mistral-7B-Instruct-v0.3
• Fine-tuning method: QLoRA (4-bit)
• Merged: LoRA weights merged into base model for inference
• Language: English
• License: Apache 2.0 (inherits from base model license)

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Intended Use

Direct Use

The model converts user messages like:

Log lunch: 4 rotis (24g each), 150g dal, 130g sabzi

Into structured JSON:

{ "intent": "log_meal", "meal_type": "lunch", "items": [ {"food_name": "Roti", "grams": 96}, {"food_name": "Dal", "grams": 150}, {"food_name": "Sabzi", "grams": 130} ] }

It is optimized to:

• Output ONLY valid JSON
• Avoid commentary
• Normalize quantities
• Multiply counts × grams automatically

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Downstream Use

Suitable for:

• Nutrition apps
• Macro tracking systems
• Health dashboards
• AI nutrition assistants
• API backends requiring structured output

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Out-of-Scope Use

Not intended for:

• Medical diagnosis
• Calorie accuracy guarantees
• Free-form conversational AI
• Open-ended creative writing
• Sensitive decision making

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Training Details

Training Data

• ~20,000 structured nutrition logging examples
• Instruction-style prompts
• JSON-formatted targets
• Explicit "JSON-only" reinforcement

Data focused on:

• Meal type detection
• Food item normalization
• Gram calculations
• Structured intent extraction

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Training Procedure

• QLoRA 4-bit fine-tuning
• LoRA adapters applied to attention layers
• Mixed precision training
• Adapter merged into base model post-training

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Training Regime

• 4-bit quantization during training
• Full precision merged model for inference
• Supervised fine-tuning (SFT)

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Inference Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Shreyasjani/nutribrain-mistral7b-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Return ONLY JSON.\n\nLog dinner: 150g paneer, 110g cooked brown rice"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

output = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.1,
    do_sample=False
)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Recommended settings:

• temperature: 0.1 – 0.3
• do_sample: False
• top_p: 0.9
• max_new_tokens: 150–300

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Bias & Limitations

• Nutrition values are not verified against real databases.
• Assumes grams when unspecified.
• English-only.
• Sensitive to prompt formatting.
• Requires explicit "Return ONLY JSON" instruction for best results.

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Technical Specifications

• Architecture: Mistral 7B
• Parameters: ~7 billion
• Context Length: 8k
• Format: Safetensors
• Compatible with: Transformers, TGI, vLLM

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Deployment

Recommended:

• Hugging Face Inference Endpoint (TGI)
• vLLM server
• Dedicated GPU inference

Minimum GPU:

• A100 40GB (recommended)
• H100 (ideal)
• 24GB GPU may work with optimized inference

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Environmental Impact

• Hardware: NVIDIA H100 / A100
• Fine-tuning duration: ~20k steps
• Adapter size: 168MB
• Merged model size: ~14.5GB

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Author

Shreyas Jani
Fintech & AI Systems Builder

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Contact

Hugging Face: https://huggingface.co/Shreyasjani