Update README.md

# 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**.

---

## 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())

README.md

@@ -8,6 +8,38 @@ tags:
 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
+      # (optional but recommended)
+      revision: <dataset_git_sha_or_tag>
+    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
 ---
 
 # Uploaded finetuned  model
@@ -18,4 +50,4 @@ language:
 
 This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface'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)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)