feat: Pull models directly from huggingface (#1206)

* Add from_pretrained method to Llama class

* Update docs

* Merge filename and pattern

README.md

@@ -212,6 +212,19 @@ Below is a short example demonstrating how to use the high-level API to for basi
 
 Text completion is available through the [`__call__`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.__call__) and [`create_completion`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_completion) methods of the [`Llama`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama) class.
 
+## Pulling models from Hugging Face
+
+You can pull `Llama` models from Hugging Face using the `from_pretrained` method.
+You'll need to install the `huggingface-hub` package to use this feature (`pip install huggingface-hub`).
+
+```python
+llama = Llama.from_pretrained(
+    repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
+    filename="*q8_0.gguf",
+    verbose=False
+)
+```
+
 ### Chat Completion
 
 The high-level API also provides a simple interface for chat completion.

docs/api-reference.md

@@ -26,6 +26,7 @@ High-level Python bindings for llama.cpp.
             - load_state
             - token_bos
             - token_eos
+            - from_pretrained
         show_root_heading: true
 
 ::: llama_cpp.LlamaGrammar

llama_cpp/llama.py

@@ -4,6 +4,8 @@ import os
 import sys
 import uuid
 import time
+import json
+import fnmatch
 import multiprocessing
 from typing import (
     List,
@@ -16,6 +18,7 @@ from typing import (
     Callable,
 )
 from collections import deque
+from pathlib import Path
 
 import ctypes
 
@@ -29,10 +32,7 @@ from .llama_cache import (
     LlamaDiskCache,  # type: ignore
     LlamaRAMCache,  # type: ignore
 )
-from .llama_tokenizer import (
-    BaseLlamaTokenizer,
-    LlamaTokenizer
-)
+from .llama_tokenizer import BaseLlamaTokenizer, LlamaTokenizer
 import llama_cpp.llama_cpp as llama_cpp
 import llama_cpp.llama_chat_format as llama_chat_format
 
@@ -50,9 +50,7 @@ from ._internals import (
     _LlamaSamplingContext,  # type: ignore
 )
 from ._logger import set_verbose
-from ._utils import (
-    suppress_stdout_stderr
-)
+from ._utils import suppress_stdout_stderr
 
 
 class Llama:
@@ -189,7 +187,11 @@ class Llama:
             Llama.__backend_initialized = True
 
         if isinstance(numa, bool):
-            self.numa = llama_cpp.GGML_NUMA_STRATEGY_DISTRIBUTE if numa else llama_cpp.GGML_NUMA_STRATEGY_DISABLED
+            self.numa = (
+                llama_cpp.GGML_NUMA_STRATEGY_DISTRIBUTE
+                if numa
+                else llama_cpp.GGML_NUMA_STRATEGY_DISABLED
+            )
         else:
             self.numa = numa
 
@@ -246,9 +248,9 @@ class Llama:
                 else:
                     raise ValueError(f"Unknown value type for {k}: {v}")
 
-            self._kv_overrides_array[
-                -1
-            ].key = b"\0"  # ensure sentinel element is zeroed
+            self._kv_overrides_array[-1].key = (
+                b"\0"  # ensure sentinel element is zeroed
+            )
             self.model_params.kv_overrides = self._kv_overrides_array
 
         self.n_batch = min(n_ctx, n_batch)  # ???
@@ -289,7 +291,9 @@ class Llama:
         )
         self.context_params.yarn_orig_ctx = yarn_orig_ctx if yarn_orig_ctx != 0 else 0
         self.context_params.mul_mat_q = mul_mat_q
-        self.context_params.logits_all = logits_all if draft_model is None else True # Must be set to True for speculative decoding
+        self.context_params.logits_all = (
+            logits_all if draft_model is None else True
+        )  # Must be set to True for speculative decoding
         self.context_params.embedding = embedding
         self.context_params.offload_kqv = offload_kqv
 
@@ -379,8 +383,14 @@ class Llama:
         if self.verbose:
             print(f"Model metadata: {self.metadata}", file=sys.stderr)
 
-        if self.chat_format is None and self.chat_handler is None and "tokenizer.chat_template" in self.metadata:
-            chat_format = llama_chat_format.guess_chat_format_from_gguf_metadata(self.metadata)
+        if (
+            self.chat_format is None
+            and self.chat_handler is None
+            and "tokenizer.chat_template" in self.metadata
+        ):
+            chat_format = llama_chat_format.guess_chat_format_from_gguf_metadata(
+                self.metadata
+            )
 
             if chat_format is not None:
                 self.chat_format = chat_format
@@ -406,9 +416,7 @@ class Llama:
                     print(f"Using chat bos_token: {bos_token}", file=sys.stderr)
 
                 self.chat_handler = llama_chat_format.Jinja2ChatFormatter(
-                    template=template,
-                    eos_token=eos_token,
-                    bos_token=bos_token
+                    template=template, eos_token=eos_token, bos_token=bos_token
                 ).to_chat_handler()
 
         if self.chat_format is None and self.chat_handler is None:
@@ -459,7 +467,9 @@ class Llama:
         """
         return self.tokenizer_.tokenize(text, add_bos, special)
 
-    def detokenize(self, tokens: List[int], prev_tokens: Optional[List[int]] = None) -> bytes:
+    def detokenize(
+        self, tokens: List[int], prev_tokens: Optional[List[int]] = None
+    ) -> bytes:
         """Detokenize a list of tokens.
 
         Args:
@@ -707,7 +717,9 @@ class Llama:
 
             if self.draft_model is not None:
                 self.input_ids[self.n_tokens : self.n_tokens + len(tokens)] = tokens
-                draft_tokens = self.draft_model(self.input_ids[:self.n_tokens + len(tokens)])
+                draft_tokens = self.draft_model(
+                    self.input_ids[: self.n_tokens + len(tokens)]
+                )
                 tokens.extend(
                     draft_tokens.astype(int)[
                         : self._n_ctx - self.n_tokens - len(tokens)
@@ -792,6 +804,7 @@ class Llama:
 
         # decode and fetch embeddings
         data: List[List[float]] = []
+
         def decode_batch(n_seq: int):
             assert self._ctx.ctx is not None
             llama_cpp.llama_kv_cache_clear(self._ctx.ctx)
@@ -800,9 +813,9 @@ class Llama:
 
             # store embeddings
             for i in range(n_seq):
-                embedding: List[float] = llama_cpp.llama_get_embeddings_ith(self._ctx.ctx, i)[
-                    :n_embd
-                ]
+                embedding: List[float] = llama_cpp.llama_get_embeddings_ith(
+                    self._ctx.ctx, i
+                )[:n_embd]
                 if normalize:
                     norm = float(np.linalg.norm(embedding))
                     embedding = [v / norm for v in embedding]
@@ -1669,6 +1682,7 @@ class Llama:
         """
         try:
             from openai.types.chat import ChatCompletion, ChatCompletionChunk
+
             stream = kwargs.get("stream", False)  # type: ignore
             assert isinstance(stream, bool)
             if stream:
@@ -1866,7 +1880,88 @@ class Llama:
                 break
         return longest_prefix
 
+    @classmethod
+    def from_pretrained(
+        cls,
+        repo_id: str,
+        filename: Optional[str],
+        local_dir: Optional[Union[str, os.PathLike[str]]] = ".",
+        local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
+        **kwargs: Any,
+    ) -> "Llama":
+        """Create a Llama model from a pretrained model name or path.
+        This method requires the huggingface-hub package.
+        You can install it with `pip install huggingface-hub`.
 
+        Args:
+            repo_id: The model repo id.
+            filename: A filename or glob pattern to match the model file in the repo.
+            local_dir: The local directory to save the model to.
+            local_dir_use_symlinks: Whether to use symlinks when downloading the model.
+            **kwargs: Additional keyword arguments to pass to the Llama constructor.
+
+        Returns:
+            A Llama model."""
+        try:
+            from huggingface_hub import hf_hub_download, HfFileSystem
+            from huggingface_hub.utils import validate_repo_id
+        except ImportError:
+            raise ImportError(
+                "Llama.from_pretrained requires the huggingface-hub package. "
+                "You can install it with `pip install huggingface-hub`."
+            )
+
+        validate_repo_id(repo_id)
+
+        hffs = HfFileSystem()
+
+        files = [
+            file["name"] if isinstance(file, dict) else file
+            for file in hffs.ls(repo_id)
+        ]
+
+        # split each file into repo_id, subfolder, filename
+        file_list: List[str] = []
+        for file in files:
+            rel_path = Path(file).relative_to(repo_id)
+            file_list.append(str(rel_path))
+
+        matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)]  # type: ignore
+
+        if len(matching_files) == 0:
+            raise ValueError(
+                f"No file found in {repo_id} that match {filename}\n\n"
+                f"Available Files:\n{json.dumps(file_list)}"
+            )
+
+        if len(matching_files) > 1:
+            raise ValueError(
+                f"Multiple files found in {repo_id} matching {filename}\n\n"
+                f"Available Files:\n{json.dumps(files)}"
+            )
+
+        (matching_file,) = matching_files
+
+        subfolder = str(Path(matching_file).parent)
+        filename = Path(matching_file).name
+
+        local_dir = "."
+
+        # download the file
+        hf_hub_download(
+            repo_id=repo_id,
+            local_dir=local_dir,
+            filename=filename,
+            subfolder=subfolder,
+            local_dir_use_symlinks=local_dir_use_symlinks,
+        )
+
+        model_path = os.path.join(local_dir, filename)
+
+        return cls(
+            model_path=model_path,
+            **kwargs,
+        )
 
 
 class LlamaState: