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Model: Aniq-63/qwen3-0.6B-recipe-finetuned
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen3-0.6B
+tags:
+- recipe-generation
+- food
+- cooking
+- fine-tuned
+- qwen3
+- unsloth
+datasets:
+- recipe_nlg
+pipeline_tag: text-generation
+---
+
+# Qwen3-0.6B Recipe Chef
+
+A fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) trained on 60,000 recipes from the RecipeNLG dataset.
+Give it a list of ingredients and it generates a complete recipe with title, quantities, and step-by-step directions.
+
+## Model Details
+
+| Property        | Value                        |
+|----------------|------------------------------|
+| Base model      | Qwen/Qwen3-0.6B              |
+| Training data   | RecipeNLG (70k samples)      |
+| Fine-tune method| LoRA (r=64, alpha=128)        |
+| Epochs          | 2                            |
+| Training loss   | 0.86                        |
+| Framework       | Unsloth + TRL                |
+
+## How to Use
+
+### Option 1 — With Unsloth (recommended, faster)
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name     = "Aniq-63/qwen3-0.6B-recipe-finetuned",
+    max_seq_length = 1024,
+    load_in_4bit   = True,
+)
+
+FastLanguageModel.for_inference(model)
+
+@torch.inference_mode()
+def generate_recipe(ingredients: str) -> str:
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a professional chef assistant. "
+                "When given a list of ingredients, generate a complete recipe with "
+                "a title, structured ingredient list with quantities, and clear "
+                "step-by-step directions."
+            )
+        },
+        {
+            "role": "user",
+            "content": ingredients
+        }
+    ]
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=False,
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens = 400,
+        temperature    = 0.7,
+        top_p          = 0.9,
+        do_sample      = True,
+        use_cache      = False,
+    )
+    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(new_tokens, skip_special_tokens=True)
+
+print(generate_recipe("chicken, garlic, onion, olive oil, tomato"))
+```
+
+### Option 2 — With standard Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Aniq-63/qwen3-0.6B-recipe-finetuned",
+    torch_dtype    = torch.float16,
+    device_map     = "auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Aniq-63/qwen3-recipe-chef")
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a professional chef assistant. "
+            "When given a list of ingredients, generate a complete recipe with "
+            "a title, structured ingredient list with quantities, and clear "
+            "step-by-step directions."
+        )
+    },
+    {
+        "role": "user",
+        "content": "chicken, garlic, onion, olive oil, tomato"
+    }
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens = 400,
+    temperature    = 0.7,
+    top_p          = 0.9,
+    do_sample      = True,
+)
+
+new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+## Training Details
+
+- **Dataset:** [RecipeNLG](https://www.kaggle.com/datasets/paultimothymooney/recipenlg)
+- **Fine-tune method:** LoRA (Unsloth)
+- **Epochs:** 2