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---
base_model:
- Qwen/Qwen2.5-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
datasets:
- joackimagno/FILIPINO_RECIPES_2K_V2
metrics:
- bleu
- rouge
- meteor
model-index:
- name: MASID-v3
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: joackimagno/FILIPINO_RECIPES_2K_V2
type: joackimagno/FILIPINO_RECIPES_2K_V2
split: test
metrics:
- name: BLEU-4
type: bleu
value: 0.07
- name: METEOR
type: meteor
value: 0.35
- name: ROUGE-L (F1)
type: rouge
value: 0.32
unit: f1
config: rougeL
---
# MASID-v3
**MASID-v3** is a fine-tuned version of **Qwen2.5-7B** trained specifically for **Filipino recipe generation**, with a focus on main dish preparation.
This model was trained on the **Filipino Recipes 2K V2 dataset**, a curated collection of ~2,000 authentic Filipino recipes.
Unlike earlier variants that explored multi-stage fine-tuning, **MASID-v3 was trained directly from Qwen2.5-7B** using this dataset to specialize the model toward Filipino culinary knowledge.
The goal of MASID-v3 is to generate structured and culturally accurate Filipino main dish recipes, covering a wide range of traditional cooking methods and ingredient combinations.
---
## Model Details
- **Base Model**: [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
- **Dataset**: Filipino Recipes 2K V2 (~2,000 samples)
- **Training Objective**: Recipe text generation (Filipino cuisine, main dishes)
- **Method**: Direct fine-tuning from Qwen2.5-7B
---
## Intended Use
- Assisting in **recipe writing**
- Exploring **Filipino food culture**
- Generating **cooking instructions** in natural language
---
## Limitations
- The model was trained on a relatively **small dataset (~2k samples)**.
- May sometimes produce **hallucinated ingredients** or **inaccurate cooking steps**.
- Not suitable for use as a **nutritional or food safety reference**.
- Best used for **research, education, and creative applications**.
---
## Evaluation
| Dataset | Split | BLEU-4 | METEOR | ROUGE-L (F1) |
|------------------------------------|:-----:|:------:|:------:|:------------:|
| joackimagno/FILIPINO_RECIPES_2K_V2 | test | 0.07 | 0.35 | 0.32 |
---
---
This Qwen2 model was trained **2× faster** with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face’s TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## Example Usage
```python
from typing import List
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
# Load model and tokenizer
model_name = "joackimagno/MASID-v3"
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()) |