Create README.md

README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- mehta/CooperLM-354M
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- toy-llm
+- gpt2
+- 4bit
+- quantized
+- casual-lm
+- transformers
+- small-llm
+---
+
+# 🧠 CooperLM-354M (4-bit Quantized)
+
+This is a 4-bit quantized version of [CooperLM-354M](https://huggingface.co/mehta/CooperLM-354M), a 354M parameter GPT-2 style language model trained from scratch on a subset of Wikipedia, BookCorpus, and OpenWebText.
+
+The quantized model is intended for faster inference and smaller memory footprint, especially useful for CPU or limited-GPU setups.
+
+---
+
+## 📌 Model Details
+
+- **Base Model**: [mehta/CooperLM-354M](https://huggingface.co/mehta/CooperLM-354M)
+- **Architecture**: GPT-2 (24 layers, 16 heads, 1024 hidden size)
+- **Quantization**: 4-bit integer weights via `AutoGPTQ` (safetensors)
+- **Precision**: INT4
+
+---
+
+## 🛠️ How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("mehta/CooperLM-354M-4bit")
+model = AutoModelForCausalLM.from_pretrained("mehta/CooperLM-354M-4bit")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+prompt = "In the distant future,"
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+outputs = model.generate(
+    **inputs,
+    max_length=100,
+    temperature=0.8,
+    top_p=0.95,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))