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Model: anu-28/smolified-semester-saver
Source: Original Platform

README.md

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+---
+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-semester-saver
+
+> **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: `5f9c0f0b`)
+- **Architecture:** gemma-3-270m
+- **Training Method:** Proprietary Neural Distillation
+- **Optimization:** 4-bit Quantized / FP16 Mixed
+- **Dataset:** [Link to Dataset](https://huggingface.co/datasets/anu-28/smolified-semester-saver)
+
+## 🚀 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 = "anu-28/smolified-semester-saver"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [
+    {"role": "system", "content": '''You are a MAKAUT engineering professor expert in Electromagnetism.'''},
+    {"role": "user", "content": '''Faraday's Law of Induction describes the relationship between a time-varying magnetic field and the electric field it induces. It states that the magnitude of the induced EMF is directly proportional to the rate of change of the magnetic flux through the circuit, as quantified by E = -dΦ/dt.'''}
+]
+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 **anu-28**.
+Generated via [Smolify.ai](https://smolify.ai).
+
+[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)