Merge branch 'main' into v0.2-wip

CHANGELOG.md

@@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.1.77]
+
+- (llama.cpp) Update llama.cpp add support for LLaMa 2 70B
+- (server) Add temporary n_gqa and rms_norm_eps parameters required for LLaMa 2 70B
+
 ## [0.1.76]
 
 - (llama.cpp) Update llama.cpp add support for LLaMa 2 70B

README.md

@@ -135,6 +135,14 @@ For instance, if you want to work with larger contexts, you can expand the conte
 llm = Llama(model_path="./models/7B/ggml-model.bin", n_ctx=2048)
 ```
 
+### Loading llama-2 70b
+
+Llama2 70b must set the `n_gqa` parameter (grouped-query attention factor) to 8 when loading:
+
+```python
+llm = Llama(model_path="./models/7B/ggml-model.bin", n_gqa=8)
+```
+
 ## Web Server
 
 `llama-cpp-python` offers a web server which aims to act as a drop-in replacement for the OpenAI API.

llama_cpp/llama.py

@@ -230,6 +230,8 @@ class Llama:
         tensor_split: Optional[List[float]] = None,
         rope_freq_base: float = 10000.0,
         rope_freq_scale: float = 1.0,
+        n_gqa: Optional[int] = None,  # (TEMPORARY) must be 8 for llama2 70b
+        rms_norm_eps: Optional[float] = None, # (TEMPORARY)
         verbose: bool = True,
     ):
         """Load a llama.cpp model from `model_path`.
@@ -291,6 +293,12 @@ class Llama:
         self.params.rope_freq_base = rope_freq_base
         self.params.rope_freq_scale = rope_freq_scale
 
+        if n_gqa is not None:
+            self.params.n_gqa = n_gqa
+
+        if rms_norm_eps is not None:
+            self.params.rms_norm_eps = rms_norm_eps
+
         self.last_n_tokens_size = last_n_tokens_size
         self.n_batch = min(n_ctx, n_batch)
 
@@ -1530,6 +1538,10 @@ class Llama:
             lora_base=self.lora_base,
             lora_path=self.lora_path,
             tensor_split=self.tensor_split,
+            ### TEMPORARY ###
+            n_gqa=self.params.n_gqa,
+            rms_norm_eps=self.params.rms_norm_eps,
+            ### TEMPORARY ###
             ### DEPRECATED ###
             n_parts=self.n_parts,
             ### DEPRECATED ###
@@ -1539,7 +1551,6 @@ class Llama:
         self.__init__(
             model_path=state["model_path"],
             n_ctx=state["n_ctx"],
-            n_parts=state["n_parts"],
             n_gpu_layers=state["n_gpu_layers"],
             seed=state["seed"],
             f16_kv=state["f16_kv"],
@@ -1556,6 +1567,13 @@ class Llama:
             lora_path=state["lora_path"],
             tensor_split=state["tensor_split"],
             verbose=state["verbose"],
+            ### TEMPORARY ###
+            n_gqa=state["n_gqa"],
+            rms_norm_eps=state["rms_norm_eps"],
+            ### TEMPORARY ###
+            ### DEPRECATED ###
+            n_parts=state["n_parts"],
+            ### DEPRECATED ###
         )
 
     def save_state(self) -> LlamaState:

llama_cpp/server/app.py

@@ -100,6 +100,14 @@ class Settings(BaseSettings):
         default=True,
         description="Whether to interrupt requests when a new request is received.",
     )
+    n_gqa: Optional[int] = Field(
+        default=None,
+        description="TEMPORARY: Set to 8 for Llama2 70B",
+    )
+    rms_norm_eps: Optional[float] = Field(
+        default=None,
+        description="TEMPORARY",
+    )
 
 
 class ErrorResponse(TypedDict):
@@ -325,6 +333,8 @@ def create_app(settings: Optional[Settings] = None):
         last_n_tokens_size=settings.last_n_tokens_size,
         vocab_only=settings.vocab_only,
         verbose=settings.verbose,
+        n_gqa=settings.n_gqa,
+        rms_norm_eps=settings.rms_norm_eps,
     )
     if settings.cache:
         if settings.cache_type == "disk":

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "llama_cpp_python"
-version = "0.1.76"
+version = "0.1.77"
 description = "Python bindings for the llama.cpp library"
 readme = "README.md"
 license = { text = "MIT" }