Update README.md

README.md

@@ -71,6 +71,112 @@ model-index:
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-7B
 
+# 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())
+
+
+
 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)