Update README.md

README.md

@@ -1,97 +1,109 @@
-
-<h1 align="center">Circuit</h1>
-<p align="center">Fine-tuned Phi-3 for Logical Reasoning</p>
-
-<p align="center">
-  <img src="https://i.postimg.cc/Nfnst2F9/Circuit.png" alt="Circuit Logo" style="max-width:100%; height:auto;">
-</p>
-
-
-# Model performance 
-
-## Benchmark
-
-
-<p align="center">
-  <img src="https://i.postimg.cc/85pjRhwf/daata.png" alt="App Screenshot" style="max-width:100%; height:auto;">
-</p>
-
-Trained on the [lucasmccabe/logiqa](https://huggingface.co/datasets/lucasmccabe/logiqa) dataset, Circuit enhances the model’s ability to reason through complex problems, answer multi-step logic questions, and provide consistent explanations.
-
-
-#  Model Details
-
-| Property | Value |
-|-----------|--------|
-| **Base model** | `microsoft/Phi-3-mini-4k-instruct` |
-| **Fine-tuned for** | Logical Reasoning |
-| **Dataset** | [`lucasmccabe/logiqa`](https://huggingface.co/datasets/lucasmccabe/logiqa) |
-| **Technique** | LoRA fine-tuning, merged for direct use |
-| **Formats available** | Full (HF Transformers) + Quantized (`.gguf` for llama.cpp / Ollama) |
-| **Project** | **Circuit** |
-| **Fine-tuned by** | Rudransh |
-
-
-
-
-
-#  Model Variants
-
-| Variant | Description | File |
-|----------|--------------|------|
-|  **Full model** | Merged LoRA with base, compatible with `transformers` | `pytorch_model.bin` |
-|  **Quantized model (GGUF)** | Optimized for CPU/GPU inference via `llama.cpp`, `text-generation-webui`, or `Ollama` | `circuit_phi3_q4.gguf` |
-
-#  Example Usage (Transformers)
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-model = AutoModelForCausalLM.from_pretrained(
-    "rudranshjoshi/circuit",
-    torch_dtype=torch.float16,
-    trust_remote_code=True
-)
-tokenizer = AutoTokenizer.from_pretrained(
-    "rudranshjoshi/circuit",
-    trust_remote_code=True
-)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-prompt = "Your prompt here"
-inputs = tokenizer(prompt, return_tensors="pt").to(device)
-outputs = model.generate(**inputs, max_new_tokens=150)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
-```
-
-
-# Training Summary
-
-Base model: Phi-3 Mini 4K Instruct
-
-Dataset: LogiQA (lucasmccabe/logiqa)
-
-Training method: LoRA fine-tuning, later merged
-
-Hardware: NVIDIA RTX 1080
-
-Epochs: ~3
-
-Objective: Improve reasoning consistency and structured explanations
-
-
-
-# Acknowledgements
-
-Microsoft
- for Phi-3
-
-Lucas McCabe
- for LogiQA dataset
-
-Fine-tuned and quantized by Rudransh under Project Circuit 
-
+---
+license: mit
+datasets:
+- lucasmccabe/logiqa
+language:
+- en
+base_model:
+- microsoft/Phi-3-mini-4k-instruct
+tags:
+- code
+- logic
+- efficiency
+---
+
+<h1 align="center">Circuit</h1>
+<p align="center">Fine-tuned Phi-3 for Logical Reasoning</p>
+
+<p align="center">
+  <img src="https://i.postimg.cc/Nfnst2F9/Circuit.png" alt="Circuit Logo" style="max-width:100%; height:auto;">
+</p>
+
+
+# Model performance 
+
+## Benchmark
+
+
+<p align="center">
+  <img src="https://i.postimg.cc/85pjRhwf/daata.png" alt="App Screenshot" style="max-width:100%; height:auto;">
+</p>
+
+Trained on the [lucasmccabe/logiqa](https://huggingface.co/datasets/lucasmccabe/logiqa) dataset, Circuit enhances the model’s ability to reason through complex problems, answer multi-step logic questions, and provide consistent explanations.
+
+
+#  Model Details
+
+| Property | Value |
+|-----------|--------|
+| **Base model** | `microsoft/Phi-3-mini-4k-instruct` |
+| **Fine-tuned for** | Logical Reasoning |
+| **Dataset** | [`lucasmccabe/logiqa`](https://huggingface.co/datasets/lucasmccabe/logiqa) |
+| **Technique** | LoRA fine-tuning, merged for direct use |
+| **Formats available** | Full (HF Transformers) + Quantized (`.gguf` for llama.cpp / Ollama) |
+| **Project** | **Circuit** |
+| **Fine-tuned by** | Rudransh |
+
+
+
+
+
+#  Model Variants
+
+| Variant | Description | File |
+|----------|--------------|------|
+|  **Full model** | Merged LoRA with base, compatible with `transformers` | `pytorch_model.bin` |
+|  **Quantized model (GGUF)** | Optimized for CPU/GPU inference via `llama.cpp`, `text-generation-webui`, or `Ollama` | `circuit_phi3_q4.gguf` |
+
+#  Example Usage (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "rudranshjoshi/circuit",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "rudranshjoshi/circuit",
+    trust_remote_code=True
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+prompt = "Your prompt here"
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+outputs = model.generate(**inputs, max_new_tokens=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+
+# Training Summary
+
+Base model: Phi-3 Mini 4K Instruct
+
+Dataset: LogiQA (lucasmccabe/logiqa)
+
+Training method: LoRA fine-tuning, later merged
+
+Hardware: NVIDIA RTX 1080
+
+Epochs: ~3
+
+Objective: Improve reasoning consistency and structured explanations
+
+
+
+# Acknowledgements
+
+Microsoft
+ for Phi-3
+
+Lucas McCabe
+ for LogiQA dataset
+
+Fine-tuned and quantized by Rudransh under Project Circuit