Qlora (#219)

qlora

llms/deepseek-coder/README.md

@@ -32,6 +32,10 @@ page](https://huggingface.co/deepseek-ai) to see a list of available models.
 By default, the conversion script will save the converted `weights.npz`,
 tokenizer, and `config.json` in the `mlx_model` directory.
 
+> [!TIP] Alternatively, you can also download a few converted checkpoints from
+> the [MLX Community](https://huggingface.co/mlx-community) organization on
+> Hugging Face and skip the conversion step.
+
 ### Run
 
 Once you've converted the weights, you can interact with the Deepseek coder

llms/llama/convert.py

@@ -14,11 +14,13 @@ import torch
 from llama import Llama, ModelArgs, sanitize_config
 from mlx.utils import tree_flatten, tree_map, tree_unflatten
 
+
 def torch_to_mx(a: torch.Tensor, *, dtype: str) -> mx.array:
     # bfloat16 is not numpy convertible. Upcast to float32 to avoid precision loss
-    a = a.to(torch.float32) if dtype == 'bfloat16' else a.to(getattr(torch, dtype))
+    a = a.to(torch.float32) if dtype == "bfloat16" else a.to(getattr(torch, dtype))
     return mx.array(a.numpy(), getattr(mx, dtype))
 
+
 def llama(model_path, *, dtype: str):
     SHARD_FIRST = ["wv", "wq", "wk", "w1", "w3", "output"]
     SHARD_SECOND = ["tok_embeddings", "wo", "w2"]
@@ -48,7 +50,7 @@ def llama(model_path, *, dtype: str):
         state = torch.load(wf, map_location=torch.device("cpu"))
         for k, v in state.items():
             v = torch_to_mx(v, dtype=dtype)
-            state[k] = None # free memory
+            state[k] = None  # free memory
             if shard_key(k) in SHARD_WEIGHTS:
                 weights[k].append(v)
             else:
@@ -204,7 +206,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--dtype",
         help="dtype for loading the torch model and input for quantization or saving the converted model. "
-             "The original weights are stored in bfloat16.",
+        "The original weights are stored in bfloat16.",
         type=str,
         default="float16",
     )