mehta/CooperLM-354M-4bit

🧠 CooperLM-354M (4-bit Quantized)

This is a 4-bit quantized version of 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.

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📌 Model Details

• Base Model: mehta/CooperLM-354M
• Architecture: GPT-2 (24 layers, 16 heads, 1024 hidden size)
• Quantization: 4-bit integer weights via AutoGPTQ (safetensors)
• Precision: INT4

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🛠️ How to Use

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))