MASID-v1.1

MASID-v1.1 is a transfer-learned Filipino main-dish recipe generator.
It is trained on top of the base model joackimagno/Qwen-2.5-General-Recipe-Generation, which itself was fine-tuned from Qwen2.5-7B using ~60k general recipes from joackimagno/general-recipes.
MASID-v1.1 then performs a second-stage fine-tuning on joackimagno/FILIPINO_RECIPES_2K (~2k) to specialize in Filipino main dish generation.

The goal is to generate structured and culturally faithful Filipino recipes while benefiting from broader cooking knowledge learned during the general-recipe stage.

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Model Details

• Base Model (stage 0): Qwen/Qwen2.5-7B
• Intermediate Model (stage 1): joackimagno/Qwen-2.5-General-Recipe-Generation — trained on ~60k general recipes
• Specialization Dataset (stage 2): joackimagno/FILIPINO_RECIPES_2K_V2 (~2,000 samples)
• Objective: Recipe text generation (Filipino cuisine, main dishes)
• Method: Transfer learning (continued fine-tuning from the general-recipe model)

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Intended Use

• Assisting in recipe writing
• Exploring Filipino food culture
• Generating cooking instructions in natural language

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Limitations

• Trained on a relatively small Filipino dataset (~2k) for the specialization stage.
• May occasionally produce hallucinated ingredients or imprecise steps.
• Not a substitute for nutrition or food-safety advice.
• Best for research, education, and creative use cases.

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Evaluation

Dataset	Split	BLEU-4	METEOR	ROUGE-L (F1)
joackimagno/FILIPINO_RECIPES_2K_V2	test	0.10	0.37	0.37

Dataset Comparison:

Dataset	Description
joackimagno/FILIPINO_RECIPES_2K	Ingredient Name excludes basic pantry items (e.g. oil, water) but includes any ingredients
joackimagno/FILIPINO_RECIPES_2K_V2	Ingredient Name only contains classified ingredients from the small object detection model

Notes: Evaluated with Alpaca-style prompting; simple post-processing (strip, EOS truncation).
If you rerun evaluation, pin dataset and package versions for reproducibility.

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This Qwen2 model was trained 2× faster with Unsloth and Hugging Face’s TRL library.

Example Usage

from typing import List
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

# Load model and tokenizer
model_name = "joackimagno/MASID-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)

# ==============================================================
# Alpaca-style prompt
# ==============================================================

SYSTEM_INSTRUCTION = (
    "You are a Filipino chef. Generate Filipino MAIN DISH recipes.\n"
    "Follow these output rules:\n"
    "1) Use standard stovetop or oven methods.\n"
    "2) Keep steps concise and logically ordered.\n"
    "3) Output FORMAT and ORDER must be exactly:\n"
    "   Recipe name, Prep time, Cook time, Total time, Servings,\n"
    "   Full Ingredients (numbered list), Instructions (numbered list)"
)

ALPACA_TEMPLATE = (
    "Below is an instruction that describes a task, paired with an input that "
    "provides further context. Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n{}\n\n### Input:\n{}\n\n### Response:\n{}"
)

def make_model_input_from_ing(ing_names: List[str]) -> str:
    return (
        "Ingredients to use: " + ", ".join(ing_names) + ".\n"
        "Task: create a Filipino main dish recipe using these ingredients. "
        "Keep steps concise, clear, and coherent."
    )

# Example input
ing_names = ["Beef", "Potato", "Sili", "Carrot", "Sayote"]

alpaca_prompt = ALPACA_TEMPLATE.format(
    SYSTEM_INSTRUCTION,
    make_model_input_from_ing(ing_names),
    ""  # leave response empty for model to generate
)

# ==============================================================
# Run inference
# ==============================================================

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

gen_config = GenerationConfig(
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

outputs = model.generate(**inputs, generation_config=gen_config)

generated = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)

print(generated.strip())