初始化项目，由ModelHub XC社区提供模型

Model: smolify/smolified-ingredient-extractor
Source: Original Platform

README.md

@@ -0,0 +1,69 @@
+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation-inference
+- transformers
+- smolify
+- dslm
+pipeline_tag: text-generation
+inference:
+  parameters:
+    temperature: 1
+    top_p: 0.95
+    top_k: 64
+---
+
+# 🤏 smolified-ingredient-extractor
+
+> **Intelligence, Distilled.**
+
+This is a **Domain Specific Language Model (DSLM)** generated by the **Smolify Foundry**.
+
+It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.
+
+## 📦 Asset Details
+- **Origin:** Smolify Foundry (Job ID: `1f92fa68`)
+- **Architecture:** gemma-3-270m
+- **Training Method:** Proprietary Neural Distillation
+- **Optimization:** 4-bit Quantized / FP16 Mixed
+- **Dataset:** [Link to Dataset](https://huggingface.co/datasets/smolify/smolified-ingredient-extractor)
+
+## 🚀 Usage (Inference)
+This model is compatible with standard inference backends like vLLM, and Hugging Face Transformers.
+
+```python
+# Example: Running your Sovereign Model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "smolify/smolified-ingredient-extractor"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [
+    {"role": "system", "content": '''Extract ingredients from the provided Indian recipe steps and return them as an alphabetical python list.'''},
+    {"role": "user", "content": '''To cook Bhindi Masala, heat oil, fry sliced okra until non-sticky, and set aside. In the same pan, saute onions and tomatoes with coriander powder, amchur powder, and salt. Fold in the okra and steam for five minutes.'''}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+)
+if "gemma-3-270m" == "gemma-3-270m":
+    text = text.removeprefix('<bos>')
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to(model.device),
+    max_new_tokens = 1000,
+    temperature = 1.0, top_p = 0.95, top_k = 64,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+## ⚖️ License & Ownership
+This model weights are a sovereign asset owned by **smolify**.
+Generated via [Smolify.ai](https://smolify.ai).
+
+[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)