diff --git a/lit_llama/sparsification.py b/lit_llama/sparsification.py
new file mode 100644
index 00000000..bea386a0
--- /dev/null
+++ b/lit_llama/sparsification.py
@@ -0,0 +1,128 @@
+# This adapts SparseGPT process: https://github.com/IST-DASLab/sparsegpt
+# E. Frantar et al SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot, https://arxiv.org/abs/2301.00774
+# portions copyright by the authors licensed under the Apache License 2.0
+
+
+import torch
+import os
+from contextlib import contextmanager
+import warnings
+import math
+
+
+class SparseGPT:
+
+    def __init__(
+            self, 
+            linear_module, 
+            sparsity,
+            prunen=0,
+            prunem=0,
+            blocksize=128,
+            percdamp=0.01,
+
+    ):
+        assert isinstance(linear_module, torch.nn.Linear)
+
+        self.linear_module = linear_module
+        self.dev = self.linear_module.weight.device
+        self.rows = linear_module.weight.shape[0]
+        self.columns = linear_module.weight.shape[1]
+        self.H = torch.zeros((self.columns, self.columns), device=self.dev)
+        self.nsamples = 0
+        self.blocksize = blocksize
+        self.sparsity = sparsity
+        self.percdamp = percdamp
+        self.prunen = prunen
+        self.prunem = prunem
+
+    def collect_input_stats(self, _1, inp, _2):
+        inp = inp[0].detach()
+        self.last_inp = inp
+        if len(inp.shape) == 2:
+            inp = inp.unsqueeze(0)
+        tmp = inp.shape[0]
+        if len(inp.shape) == 3:
+            inp = inp.reshape((-1, inp.shape[-1]))
+        inp = inp.t()
+        self.H *= self.nsamples / (self.nsamples + tmp)
+        self.nsamples += tmp
+        inp = math.sqrt(2 / self.nsamples) * inp.float()
+        self.H += inp.matmul(inp.t())
+
+    def sparsify(self):
+
+        W = self.linear_module.weight.detach().to(dtype=torch.float, copy=True)
+        H = self.H
+        del self.H
+        dead = torch.diag(H) == 0
+        H[dead, dead] = 1
+        W[:, dead] = 0
+
+        Losses = torch.zeros_like(W)
+        Q = torch.zeros_like(W)
+
+        damp = self.percdamp * torch.mean(torch.diag(H))
+        diag = torch.arange(self.columns, device=self.dev)
+        H[diag, diag] += damp
+        H = torch.linalg.cholesky(H)
+        H = torch.cholesky_inverse(H)
+        H = torch.linalg.cholesky(H, upper=True)
+        Hinv = H
+
+        mask = None
+
+        for i1 in range(0, self.columns, self.blocksize):
+            i2 = min(i1 + self.blocksize, self.columns)
+            count = i2 - i1
+
+            W1 = W[:, i1:i2].clone()
+            Q1 = torch.zeros_like(W1)
+            Err1 = torch.zeros_like(W1)
+            Losses1 = torch.zeros_like(W1)
+            Hinv1 = Hinv[i1:i2, i1:i2]
+
+
+            if self.prunen == 0: 
+                if mask is not None:
+                    mask1 = mask[:, i1:i2]
+                else:
+                    tmp = W1 ** 2 / (torch.diag(Hinv1).reshape((1, -1))) ** 2
+                    thresh = torch.sort(tmp.flatten())[0][int(tmp.numel() * self.sparsity)]
+                    mask1 = tmp <= thresh
+            else:
+                mask1 = torch.zeros_like(W1) == 1
+
+            for i in range(count):
+                w = W1[:, i]
+                d = Hinv1[i, i]
+
+                if self.prunen != 0 and i % self.prunem == 0:
+                    tmp = W1[:, i:(i + self.prunem)] ** 2 / (torch.diag(Hinv1)[i:(i + self.prunem)].reshape((1, -1))) ** 2
+                    mask1.scatter_(1, i + torch.topk(tmp, self.prunen, dim=1, largest=False)[1], True)
+
+                q = w.clone()
+                q[mask1[:, i]] = 0
+
+
+                Q1[:, i] = q
+                Losses1[:, i] = (w - q) ** 2 / d ** 2
+
+                err1 = (w - q) / d
+                W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
+                Err1[:, i] = err1
+        
+            Q[:, i1:i2] = Q1
+            Losses[:, i1:i2] = Losses1 / 2
+
+            W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
+
+        pruned_weights = Q.reshape(self.linear_module.weight.shape).to(
+                        self.linear_module.weight.data.dtype
+        )
+        
+        # set the linear module weights to pruned weights
+        self.linear_module.weight.data = pruned_weights
+        error = torch.sum(Losses).item()
+        return error
+
diff --git a/sparsify/sparsegpt.py b/sparsify/sparsegpt.py
new file mode 100644
index 00000000..036c1a80
--- /dev/null
+++ b/sparsify/sparsegpt.py
@@ -0,0 +1,257 @@
+# This adapts SparseGPT process: https://github.com/IST-DASLab/sparsegpt
+# E. Frantar et al SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot, https://arxiv.org/abs/2301.00774
+# portions copyright by the authors licensed under the Apache License 2.0
+
+
+import gc
+import sys
+import time
+from pathlib import Path
+from typing import Optional
+
+import torch
+from datasets import load_dataset
+
+wd = Path(__file__).parent.parent.resolve()
+sys.path.append(str(wd))
+
+from lit_llama import LLaMA, Tokenizer
+from lit_llama.sparsification import SparseGPT
+
+from lit_llama.utils import EmptyInitOnDevice, llama_model_lookup
+
+
+def get_sample_data():
+    traindata = load_dataset(
+        "allenai/c4",
+        "allenai--c4",
+        data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
+        split="train",
+    )
+    # heuristic for the data size?
+    txt = "\n".join(
+        traindata[i]["text"] for i in torch.randperm(len(traindata))[:1000].tolist()
+    )
+    return txt
+
+@torch.no_grad()
+def llama_blockwise_sparsification(
+    model, 
+    sample_inputs, 
+    working_device,
+    *,
+    sparsity,
+    prunen=0,
+    prunem=0,
+    
+):
+    
+    print('Getting Inputs for the first block')
+    model.transformer.wte.to(working_device)
+    sample_inputs = sample_inputs.to(working_device)
+    inps = model.transformer.wte(sample_inputs)
+    model.transformer.wte.to("cpu")
+    torch.cuda.empty_cache()
+
+    rope_cache = model.build_rope_cache(sample_inputs)
+    mask_cache = model.build_mask_cache(sample_inputs)
+
+    print('Starting to sparsify block')
+    outs = torch.zeros_like(inps)
+
+
+    submodules_to_process = [
+        "attn.c_attn",
+        "attn.c_proj",
+        "mlp.c_fc1",
+        "mlp.c_fc2",
+        "mlp.c_proj",
+    ]
+
+
+    for i, block in enumerate(model.transformer.h):
+
+        block.to(working_device)
+
+        for name in submodules_to_process:
+            print(i, name, end=" ")
+            t0 = time.perf_counter()
+            print("collecting stats", end=" ")
+            sys.stdout.flush()
+            module = block.get_submodule(name)
+
+            sparsegpt = SparseGPT(
+                module,
+                sparsity=sparsity,
+                prunen=prunen,
+                prunem=prunem,
+            )
+
+            handle = module.register_forward_hook(sparsegpt.collect_input_stats)
+           
+            for j in range(inps.size(0)):
+                outs[j : j + 1], _ = block(
+                    inps[j : j + 1],
+                    rope=rope_cache,
+                    mask=mask_cache,
+                    max_seq_length=model.config.block_size
+                )
+
+            handle.remove()
+
+            
+            print("sparsifying", end=" ")
+            sys.stdout.flush()
+            error = sparsegpt.sparsify()
+
+            del sparsegpt
+            gc.collect()
+            torch.cuda.empty_cache()
+            t1 = time.perf_counter()
+            print(f"time {int(t1 - t0 + 0.5)}s sparsification error {error:.1f}")
+
+        
+        for j in range(inps.size(0)):
+            outs[j : j + 1], _ = block(
+                inps[j : j + 1],
+                rope=rope_cache,
+                mask=mask_cache,
+                max_seq_length=model.config.block_size
+            )
+
+        block.cpu()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        inps, outs = outs, inps
+
+    model.transformer.ln_f.to(working_device)
+    for j in range(inps.size(0)):
+        outs[j : j + 1] = model.transformer.ln_f(inps[j : j + 1])
+    model.transformer.ln_f.to("cpu")
+
+    # normalised out will be input to the LM head
+    inps, outs = outs, inps
+
+    model.lm_head.to(working_device)
+    sparsegpt = SparseGPT(
+        model.lm_head,
+        sparsity=sparsity,
+        prunen=prunen,
+        prunem=prunem,
+    )
+
+    # During the forward pass, the collect_input_stats function collects input statistics and updates the Hessian matrix.
+    handle = model.lm_head.register_forward_hook(sparsegpt.collect_input_stats)
+    for j in range(inps.size(0)):
+        model.lm_head(inps[j : j + 1])
+    handle.remove()
+    # After the forward pass, the sparsify function can be called to perform the  sparsification based on the collected statistics.
+    error = sparsegpt.sparsify()
+    model.lm_head.to("cpu")
+
+def main(
+    *,
+    checkpoint_path: Path = Path("checkpoints/lit-llama/7B/lit-llama.pth"),
+    output_path: Optional[Path] = None,
+    tokenizer_path: Path = Path("checkpoints/lit-llama/tokenizer.model"),
+    n_samples: int = 128,
+    dtype: str = "float32",
+    sparsity: float = 0.1,
+    prunem: int = 0,
+    prunen: int = 0
+) -> None:
+    """
+       Generates text samples based on a pre-trained LLaMA model and tokenizer.
+
+    Args:
+        checkpoint_path: The checkpoint path to load.
+        output_path: Path to write the sparsified model's state dict to.
+        tokenizer_path: The tokenizer path to load.
+        n_samples: Number of example inputs to use for statistics (default: 128)
+        dtype: The dtype to use to load the model.
+        sparsity: Target sparsity
+        prunem: M for N:M pruning.
+        prunen: N for N:M pruning.
+    """
+    assert checkpoint_path.is_file()
+    assert tokenizer_path.is_file()
+    if output_path is None:
+        output_path = checkpoint_path.parent / "llama-gpt-sparsified.pth"
+    assert output_path.parent.is_dir() and (not output_path.exists() or output_path.is_file())
+
+    device = "cuda"
+
+    dt = getattr(torch, dtype, None)
+    if not isinstance(dt, torch.dtype):
+        raise ValueError(f"{dtype} is not a valid dtype.")
+    dtype = dt
+
+
+    # we avoid loading the entire model on the GPU and do this block by block
+    with EmptyInitOnDevice(
+        device="cpu",
+        dtype=dtype,
+    ):
+        print("Loading model ...", file=sys.stderr)
+        t0 = time.time()
+        checkpoint = torch.load(checkpoint_path)
+        name = llama_model_lookup(checkpoint)
+        model = LLaMA.from_name(name)
+        model.load_state_dict(checkpoint)
+        print(f"Time to load model: {time.time() - t0:.02f} seconds.", file=sys.stderr)
+
+    model.eval()
+
+    tokenizer = Tokenizer(tokenizer_path)
+
+    test_string = get_sample_data()
+    encoded_text = tokenizer.encode(
+        test_string,
+        bos=True,
+        eos=False, 
+    )
+
+    block_size = 2048  
+    # truncate the text and reshape to batch by sequence length
+    encoded_text = encoded_text[: n_samples * block_size].reshape(n_samples, block_size)
+
+    t0 = time.perf_counter()
+    llama_blockwise_sparsification(
+                                   model=model, 
+                                   sample_inputs=encoded_text, 
+                                   working_device=device,
+                                   sparsity=sparsity,
+                                   prunen=prunen,
+                                   prunem=prunem
+                                   )
+    t = time.perf_counter() - t0
+
+    print(
+        f"\n\nTime for sparsification: {t:.02f} sec total",
+        file=sys.stderr,
+    )
+    print(
+        f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB",
+        file=sys.stderr,
+    )
+
+    torch.save(model.state_dict(), output_path)
+
+
+if __name__ == "__main__":
+    from jsonargparse import CLI
+
+    torch.set_float32_matmul_precision("high")
+    CLI(main)
+
+
+   
+
+
+
+
+
+
+
+