feat(server): Add support for pulling models from Huggingface Hub (#1222)

* Basic support for hf pull on server

* Add hf_model_repo_id setting

* Update README

Author: abetlen

README.md

@@ -577,6 +577,12 @@ python3 -m llama_cpp.server --model models/7B/llama-model.gguf --chat_format cha
 That will format the prompt according to how model expects it. You can find the prompt format in the model card.
 For possible options, see [llama_cpp/llama_chat_format.py](llama_cpp/llama_chat_format.py) and look for lines starting with "@register_chat_format".
 
+If you have `huggingface-hub` installed, you can also use the `--hf_model_repo_id` flag to load a model from the Hugging Face Hub.
+
+```bash
+python3 -m llama_cpp.server --hf_model_repo_id Qwen/Qwen1.5-0.5B-Chat-GGUF --model '*q8_0.gguf'
+```
+
 ### Web Server Features
 
 - [Local Copilot replacement](https://llama-cpp-python.readthedocs.io/en/latest/server/#code-completion)

llama_cpp/server/model.py

@@ -120,9 +120,20 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama:
                         kv_overrides[key] = float(value)
                     else:
                         raise ValueError(f"Unknown value type {value_type}")
+        
+        import functools
 
-        _model = llama_cpp.Llama(
-            model_path=settings.model,
+        kwargs = {}
+
+        if settings.hf_model_repo_id is not None:
+            create_fn = functools.partial(llama_cpp.Llama.from_pretrained, repo_id=settings.hf_model_repo_id, filename=settings.model)
+        else:
+            create_fn = llama_cpp.Llama
+            kwargs["model_path"] = settings.model
+        
+
+        _model = create_fn(
+            **kwargs,
             # Model Params
             n_gpu_layers=settings.n_gpu_layers,
             main_gpu=settings.main_gpu,

llama_cpp/server/settings.py

@@ -143,6 +143,11 @@ class ModelSettings(BaseSettings):
         default=None,
         description="The model name or path to a pretrained HuggingFace tokenizer model. Same as you would pass to AutoTokenizer.from_pretrained().",
     )
+    # Loading from HuggingFace Model Hub
+    hf_model_repo_id: Optional[str] = Field(
+        default=None,
+        description="The model repo id to use for the HuggingFace tokenizer model.",
+    )
     # Speculative Decoding
     draft_model: Optional[str] = Field(
         default=None,