Merge https://github.com/abetlen/llama-cpp-python

* Use jinja formatter to load chat format from gguf

* Fix off-by-one error in metadata loader

* Implement chat format auto-detection

CHANGELOG.md

@@ -7,6 +7,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.2.37]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@fea4fd4ba7f6b754ac795387b275e1a014a77bde
+- feat: Automatically set chat format from gguf by @abetlen in #1110
+
+## [0.2.36]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@2aed77eb06a329f0d82bb1c467f4244904d4073f
+- feat: Add mistral instruct chat format as "mistral-instruct" by @Rafaelblsilva in #799
+
+## [0.2.35]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@d2f650cb5b04ee2726663e79b47da5efe196ce00
+
+## [0.2.34]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@6db2b41a76ee78d5efdd5c3cddd5d7ad3f646855
+- feat: Add json schema mode by @abetlen in #1122
+
 ## [0.2.33]
 
 - feat: Update llama.cpp to ggerganov/llama.cpp@faa3526a1eba458120987ed8269e5616385a76f4

Makefile

@@ -27,6 +27,15 @@ build.blis:
 build.metal:
 	CMAKE_ARGS="-DLLAMA_METAL=on" python3 -m pip install --verbose -e .
 
+build.vulkan:
+	CMAKE_ARGS="-DLLAMA_VULKAN=on" python3 -m pip install --verbose -e .
+
+build.kompute:
+	CMAKE_ARGS="-DLLAMA_KOMPUTE=on" python3 -m pip install --verbose -e .
+
+build.sycl:
+	CMAKE_ARGS="-DLLAMA_SYCL=on" python3 -m pip install --verbose -e .
+
 build.sdist:
 	python3 -m build --sdist
 

README.md

@@ -12,20 +12,17 @@ This package provides:
 
 - Low-level access to C API via `ctypes` interface.
 - High-level Python API for text completion
-    - OpenAI-like API
-    - [LangChain compatibility](https://python.langchain.com/docs/integrations/llms/llamacpp)
-    - [LlamaIndex compatibility](https://docs.llamaindex.ai/en/stable/examples/llm/llama_2_llama_cpp.html)
+  - OpenAI-like API
+  - [LangChain compatibility](https://python.langchain.com/docs/integrations/llms/llamacpp)
+  - [LlamaIndex compatibility](https://docs.llamaindex.ai/en/stable/examples/llm/llama_2_llama_cpp.html)
 - OpenAI compatible web server
-    - [Local Copilot replacement](https://llama-cpp-python.readthedocs.io/en/latest/server/#code-completion)
-    - [Function Calling support](https://llama-cpp-python.readthedocs.io/en/latest/server/#function-calling)
-    - [Vision API support](https://llama-cpp-python.readthedocs.io/en/latest/server/#multimodal-models)
-    - [Multiple Models](https://llama-cpp-python.readthedocs.io/en/latest/server/#configuration-and-multi-model-support)
+  - [Local Copilot replacement](https://llama-cpp-python.readthedocs.io/en/latest/server/#code-completion)
+  - [Function Calling support](https://llama-cpp-python.readthedocs.io/en/latest/server/#function-calling)
+  - [Vision API support](https://llama-cpp-python.readthedocs.io/en/latest/server/#multimodal-models)
+  - [Multiple Models](https://llama-cpp-python.readthedocs.io/en/latest/server/#configuration-and-multi-model-support)
 
 Documentation is available at [https://llama-cpp-python.readthedocs.io/en/latest](https://llama-cpp-python.readthedocs.io/en/latest).
 
-
-
-
 ## Installation
 
 `llama-cpp-python` can be installed directly from PyPI as a source distribution by running:
@@ -38,7 +35,6 @@ This will build `llama.cpp` from source using cmake and your system's c compiler
 
 If you run into issues during installation add the `--verbose` flag to the `pip install` command to see the full cmake build log.
 
-
 ### Installation with Specific Hardware Acceleration (BLAS, CUDA, Metal, etc)
 
 The default pip install behaviour is to build `llama.cpp` for CPU only on Linux and Windows and use Metal on MacOS.
@@ -71,7 +67,7 @@ CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-
 
 #### cuBLAS
 
-To install with cuBLAS, set the `LLAMA_CUBLAS=1` environment variable before installing:
+To install with cuBLAS, set the `LLAMA_CUBLAS=on` environment variable before installing:
 
 ```bash
 CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
@@ -87,7 +83,7 @@ CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
 
 #### CLBlast
 
-To install with CLBlast, set the `LLAMA_CLBLAST=1` environment variable before installing:
+To install with CLBlast, set the `LLAMA_CLBLAST=on` environment variable before installing:
 
 ```bash
 CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
@@ -101,13 +97,37 @@ To install with hipBLAS / ROCm support for AMD cards, set the `LLAMA_HIPBLAS=on`
 CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
 ```
 
+#### Vulkan
+
+To install with Vulkan support, set the `LLAMA_VULKAN=on` environment variable before installing:
+
+```bash
+CMAKE_ARGS="-DLLAMA_VULKAN=on" pip install llama-cpp-python
+```
+
+#### Kompute
+
+To install with Kompute support, set the `LLAMA_KOMPUTE=on` environment variable before installing:
+
+```bash
+CMAKE_ARGS="-DLLAMA_KOMPUTE=on" pip install llama-cpp-python
+```
+
+#### SYCL
+
+To install with SYCL support, set the `LLAMA_SYCL=on` environment variable before installing:
+
+```bash
+CMAKE_ARGS="-DLLAMA_SYCL=on" pip install llama-cpp-python
+```
+
 ### Windows Notes
 
 If you run into issues where it complains it can't find `'nmake'` `'?'` or CMAKE_C_COMPILER, you can extract w64devkit as [mentioned in llama.cpp repo](https://github.com/ggerganov/llama.cpp#openblas) and add those manually to CMAKE_ARGS before running `pip` install:
 
 ```ps
 $env:CMAKE_GENERATOR = "MinGW Makefiles"
-$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on -DCMAKE_C_COMPILER=C:/w64devkit/bin/gcc.exe -DCMAKE_CXX_COMPILER=C:/w64devkit/bin/g++.exe" 
+$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on -DCMAKE_C_COMPILER=C:/w64devkit/bin/gcc.exe -DCMAKE_CXX_COMPILER=C:/w64devkit/bin/g++.exe"
 ```
 
 See the above instructions and set `CMAKE_ARGS` to the BLAS backend you want to use.
@@ -157,7 +177,7 @@ Below is a short example demonstrating how to use the high-level API to for basi
 >>> from llama_cpp import Llama
 >>> llm = Llama(
       model_path="./models/7B/llama-model.gguf",
-      # n_gpu_layers=-1, # Uncomment to use GPU acceleration 
+      # n_gpu_layers=-1, # Uncomment to use GPU acceleration
       # seed=1337, # Uncomment to set a specific seed
       # n_ctx=2048, # Uncomment to increase the context window
 )
@@ -216,6 +236,59 @@ Note that `chat_format` option must be set for the particular model you are usin
 
 Chat completion is available through the [`create_chat_completion`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.create_chat_completion) method of the [`Llama`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama) class.
 
+### JSON and JSON Schema Mode
+
+If you want to constrain chat responses to only valid JSON or a specific JSON Schema you can use the `response_format` argument to the `create_chat_completion` method.
+
+#### JSON Mode
+
+The following example will constrain the response to be valid JSON.
+
+```python
+>>> from llama_cpp import Llama
+>>> llm = Llama(model_path="path/to/model.gguf", chat_format="chatml")
+>>> llm.create_chat_completion(
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful assistant that outputs in JSON.",
+        },
+        {"role": "user", "content": "Who won the world series in 2020"},
+    ],
+    response_format={
+        "type": "json_object",
+    },
+    temperature=0.7,
+)
+```
+
+#### JSON Schema Mode
+
+To constrain the response to a specific JSON Schema, you can use the `schema` property of the `response_format` argument.
+
+```python
+>>> from llama_cpp import Llama
+>>> llm = Llama(model_path="path/to/model.gguf", chat_format="chatml")
+>>> llm.create_chat_completion(
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful assistant that outputs in JSON.",
+        },
+        {"role": "user", "content": "Who won the world series in 2020"},
+    ],
+    response_format={
+        "type": "json_object",
+        "schema": {
+            "type": "object",
+            "properties": {"team_name": {"type": "string"}},
+            "required": ["team_name"],
+        },
+    },
+    temperature=0.7,
+)
+```
+
 ### Function Calling
 
 The high-level API also provides a simple interface for function calling.
@@ -223,7 +296,6 @@ The high-level API also provides a simple interface for function calling.
 Note that the only model that supports full function calling at this time is "functionary".
 The gguf-converted files for this model can be found here: [functionary-7b-v1](https://huggingface.co/abetlen/functionary-7b-v1-GGUF)
 
-
 ```python
 >>> from llama_cpp import Llama
 >>> llm = Llama(model_path="path/to/functionary/llama-model.gguf", chat_format="functionary")
@@ -232,7 +304,7 @@ The gguf-converted files for this model can be found here: [functionary-7b-v1](h
         {
           "role": "system",
           "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"
-          
+
         },
         {
           "role": "user",
@@ -271,7 +343,6 @@ The gguf-converted files for this model can be found here: [functionary-7b-v1](h
 
 ### Multi-modal Models
 
-
 `llama-cpp-python` supports the llava1.5 family of multi-modal models which allow the language model to
 read information from both text and images.
 
@@ -317,7 +388,6 @@ For instance, if you want to work with larger contexts, you can expand the conte
 llm = Llama(model_path="./models/7B/llama-model.gguf", n_ctx=2048)
 ```
 
-
 ## OpenAI Compatible Web Server
 
 `llama-cpp-python` offers a web server which aims to act as a drop-in replacement for the OpenAI API.
@@ -365,7 +435,8 @@ A Docker image is available on [GHCR](https://ghcr.io/abetlen/llama-cpp-python).
 ```bash
 docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/llama-model.gguf ghcr.io/abetlen/llama-cpp-python:latest
 ```
-[Docker on termux (requires root)](https://gist.github.com/FreddieOliveira/efe850df7ff3951cb62d74bd770dce27) is currently the only known way to run this on phones, see [termux support issue](https://github.com/abetlen/llama-cpp-python/issues/389) 
+
+[Docker on termux (requires root)](https://gist.github.com/FreddieOliveira/efe850df7ff3951cb62d74bd770dce27) is currently the only known way to run this on phones, see [termux support issue](https://github.com/abetlen/llama-cpp-python/issues/389)
 
 ## Low-level API
 
@@ -393,7 +464,6 @@ Below is a short example demonstrating how to use the low-level API to tokenize
 
 Check out the [examples folder](examples/low_level_api) for more examples of using the low-level API.
 
-
 ## Documentation
 
 Documentation is available via [https://llama-cpp-python.readthedocs.io/](https://llama-cpp-python.readthedocs.io/).

examples/high_level_api/fastapi_server.py

@@ -9,7 +9,7 @@ export MODEL=../models/7B/...
 
 Then run:
 ```
-uvicorn llama_cpp.server.app:app --reload
+uvicorn --factory llama_cpp.server.app:create_app --reload
 ```
 
 or

llama_cpp/__init__.py

@@ -1,4 +1,4 @@
 from .llama_cpp import *
 from .llama import *
 
-__version__ = "0.2.33"
+__version__ = "0.2.37"

llama_cpp/_internals.py

@@ -216,13 +216,13 @@ class _LlamaModel:
         for i in range(llama_cpp.llama_model_meta_count(self.model)):
             nbytes = llama_cpp.llama_model_meta_key_by_index(self.model, i, buffer, buffer_size)
             if nbytes > buffer_size:
-                buffer_size = nbytes
+                buffer_size = nbytes + 1
                 buffer = ctypes.create_string_buffer(buffer_size)
                 nbytes = llama_cpp.llama_model_meta_key_by_index(self.model, i, buffer, buffer_size)
             key = buffer.value.decode("utf-8")
             nbytes = llama_cpp.llama_model_meta_val_str_by_index(self.model, i, buffer, buffer_size)
             if nbytes > buffer_size:
-                buffer_size = nbytes
+                buffer_size = nbytes + 1
                 buffer = ctypes.create_string_buffer(buffer_size)
                 nbytes = llama_cpp.llama_model_meta_val_str_by_index(self.model, i, buffer, buffer_size)
             value = buffer.value.decode("utf-8")

llama_cpp/llama.py

@@ -87,7 +87,7 @@ class Llama:
         # Backend Params
         numa: bool = False,
         # Chat Format Params
-        chat_format: str = "llama-2",
+        chat_format: Optional[str] = None,
         chat_handler: Optional[llama_chat_format.LlamaChatCompletionHandler] = None,
         # Misc
         verbose: bool = True,
@@ -343,6 +343,41 @@ 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 chat_format is not None:
+                self.chat_format = chat_format
+                if self.verbose:
+                    print(f"Guessed chat format: {chat_format}", file=sys.stderr)
+            else:
+                template = self.metadata["tokenizer.chat_template"]
+                try:
+                    eos_token_id = int(self.metadata["tokenizer.ggml.eos_token_id"])
+                except:
+                    eos_token_id = self.token_eos()
+                try:
+                    bos_token_id = int(self.metadata["tokenizer.ggml.bos_token_id"])
+                except:
+                    bos_token_id = self.token_bos()
+
+                eos_token = self.detokenize([eos_token_id]).decode("utf-8")
+                bos_token = self.detokenize([bos_token_id]).decode("utf-8")
+
+                if self.verbose:
+                    print(f"Using chat template: {template}", file=sys.stderr)
+                    print(f"Using chat eos_token: {eos_token}", file=sys.stderr)
+                    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
+                ).to_chat_handler()
+
+        if self.chat_format is None and self.chat_handler is None:
+            self.chat_format = "llama-2"
+
     @property
     def ctx(self) -> llama_cpp.llama_context_p:
         assert self._ctx.ctx is not None

llama_cpp/llama_chat_format.py

@@ -14,6 +14,20 @@ import llama_cpp.llama_grammar as llama_grammar
 
 from ._utils import suppress_stdout_stderr, Singleton
 
+### Common Chat Templates and Special Tokens ###
+
+# Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
+CHATML_CHAT_TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+CHATML_BOS_TOKEN = "<s>"
+CHATML_EOS_TOKEN = "<|im_end|>"
+
+# Source: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
+MISTRAL_INSTRUCT_CHAT_TEMPLATE = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
+MISTRAL_INSTRUCT_BOS_TOKEN = "<s>"
+MISTRAL_INSTRUCT_EOS_TOKEN = "</s>"
+
+
+### Chat Completion Handler ###
 
 class LlamaChatCompletionHandler(Protocol):
     """Base Protocol for a llama chat completion handler.
@@ -118,7 +132,6 @@ def register_chat_completion_handler(name: str):
 
 ### Chat Formatter ###
 
-
 @dataclasses.dataclass
 class ChatFormatterResponse:
     """Dataclass that stores completion parameters for a given chat format and
@@ -172,16 +185,17 @@ class Jinja2ChatFormatter(ChatFormatter):
         messages: List[llama_types.ChatCompletionRequestMessage],
         **kwargs: Any,
     ) -> ChatFormatterResponse:
-        if self.add_generation_prompt:
-            messages = [
-                *messages,
-                llama_types.ChatCompletionRequestAssistantMessage(
-                    role="assistant", content=""
-                ),
-            ]
+        def raise_exception(message: str):
+            raise ValueError(message)
+
         prompt = self._environment.render(
-            messages=messages, eos_token=self.eos_token, bos_token=self.bos_token
+            messages=messages,
+            eos_token=self.eos_token,
+            bos_token=self.bos_token,
+            raise_exception=raise_exception,
+            add_generation_prompt=self.add_generation_prompt
         )
+
         return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token])
 
     def to_chat_handler(self) -> LlamaChatCompletionHandler:
@@ -318,7 +332,14 @@ def chat_formatter_to_chat_completion_handler(
             stop = stop + rstop
 
         if response_format is not None and response_format["type"] == "json_object":
-            grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
+            try:
+                # create grammar from json schema
+                if "schema" in response_format:
+                    grammar = llama_grammar.LlamaGrammar.from_json_schema(
+                        json.dumps(response_format["schema"])
+                    )
+            except Exception as e:
+                grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
 
         completion_or_chunks = llama.create_completion(
             prompt=prompt,
@@ -433,7 +454,20 @@ def hf_tokenizer_config_to_chat_completion_handler(
     return chat_formatter_to_chat_completion_handler(chat_formatter)
 
 
+def guess_chat_format_from_gguf_metadata(metadata: Dict[str, str]) -> Optional[str]:
+    if "tokenizer.chat_template" not in metadata:
+        return None
+
+    if metadata["tokenizer.chat_template"] == CHATML_CHAT_TEMPLATE:
+        return "chatml"
+
+    if metadata["tokenizer.chat_template"] == MISTRAL_INSTRUCT_CHAT_TEMPLATE: 
+        return "mistral-instruct"
+
+    return None
+
 ### Utility functions for formatting chat prompts ###
+# TODO: Replace these with jinja2 templates
 
 
 def _get_system_message(
@@ -870,6 +904,24 @@ def format_chatml(
     return ChatFormatterResponse(prompt=_prompt, stop=_sep)
 
 
+@register_chat_format("mistral-instruct")
+def format_mistral_instruct(
+    messages: List[llama_types.ChatCompletionRequestMessage],
+    **kwargs: Any,
+) -> ChatFormatterResponse:
+    bos = "<s>"
+    eos = "</s>"
+    stop = eos
+    prompt = bos
+    for message in messages:
+        if message["role"] == "user" and message["content"] is not None and isinstance(message["content"], str):
+            prompt += "[INST] " + message["content"]
+        elif message["role"] == "assistant" and message["content"] is not None and isinstance(message["content"], str):
+            prompt += " [/INST]" + message["content"] + eos
+    prompt += " [/INST]"
+    return ChatFormatterResponse(prompt=prompt, stop=stop)
+
+
 @register_chat_format("chatglm3")
 def format_chatglm3(
     messages: List[llama_types.ChatCompletionRequestMessage],
@@ -904,7 +956,6 @@ def format_openchat(
     _prompt = _format_chatml(system_message, _messages, _sep)
     return ChatFormatterResponse(prompt=_prompt, stop=_sep)
 
-
 # Chat format for Saiga models, see more details and available models:
 # https://huggingface.co/collections/IlyaGusev/saiga2-saigamistral-6505d4ccc3d1e53166b636cd
 @register_chat_format("saiga")
@@ -926,6 +977,7 @@ def format_saiga(
     _prompt += "<s>bot"
     return ChatFormatterResponse(prompt=_prompt.strip())
 
+# Tricky chat formats that require custom chat handlers
 
 @register_chat_completion_handler("functionary")
 def functionary_chat_handler(
@@ -1434,10 +1486,14 @@ class Llava15ChatHandler:
         prompt = llama.input_ids[: llama.n_tokens].tolist()
 
         if response_format is not None and response_format["type"] == "json_object":
-            with suppress_stdout_stderr(disable=self.verbose):
-                grammar = llama_grammar.LlamaGrammar.from_string(
-                    llama_grammar.JSON_GBNF
-                )
+            try:
+                # create grammar from json schema
+                if "schema" in response_format:
+                    grammar = llama_grammar.LlamaGrammar.from_json_schema(
+                        json.dumps(response_format["schema"])
+                    )
+            except Exception as e:
+                grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
 
         return _convert_completion_to_chat(
             llama.create_completion(

llama_cpp/llama_cpp.py

@@ -93,14 +93,12 @@ c_size_t_p = POINTER(c_size_t)
 
 # from ggml-backend.h
 # typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
-ggml_backend_sched_eval_callback = ctypes.CFUNCTYPE(
-    c_bool, c_void_p, c_bool, c_void_p
-)
+ggml_backend_sched_eval_callback = ctypes.CFUNCTYPE(c_bool, c_void_p, c_bool, c_void_p)
 
 # llama.h bindings
 
 _lib.llama_max_devices.argtypes = []
-_lib.llama_max_devices.restype = ctypes.c_int32
+_lib.llama_max_devices.restype = ctypes.c_size_t
 
 LLAMA_MAX_DEVICES = _lib.llama_max_devices()
 
@@ -189,6 +187,7 @@ LLAMA_TOKEN_TYPE_BYTE = 6
 #     LLAMA_FTYPE_MOSTLY_IQ2_XS        = 20, // except 1d tensors
 #     LLAMA_FTYPE_MOSTLY_Q2_K_S        = 21, // except 1d tensors
 #     LLAMA_FTYPE_MOSTLY_Q3_K_XS       = 22, // except 1d tensors
+#     LLAMA_FTYPE_MOSTLY_IQ3_XXS       = 23, // except 1d tensors
 
 #     LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
 # };
@@ -213,6 +212,7 @@ LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19
 LLAMA_FTYPE_MOSTLY_IQ2_XS = 20
 LLAMA_FTYPE_MOSTLY_Q2_K_S = 21
 LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22
+LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23
 LLAMA_FTYPE_GUESSED = 1024
 
 # enum llama_rope_scaling_type {
@@ -390,7 +390,7 @@ class llama_model_kv_override(Structure):
 #     // LLAMA_SPLIT_LAYER: ignored
 #     int32_t main_gpu;
 
-#     // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
+#     // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
 #     const float * tensor_split;
 
 #     // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
@@ -417,7 +417,7 @@ class llama_model_params(Structure):
         n_gpu_layers (int): number of layers to store in VRAM
         split_mode (int): how to split the model across multiple GPUs
         main_gpu (int): the GPU that is used for the entire model. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored
-        tensor_split (ctypes.Array[ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES
+        tensor_split (ctypes.Array[ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
         progress_callback (llama_progress_callback): called with a progress value between 0.0 and 1.0. Pass NULL to disable. If the provided progress_callback returns true, model loading continues. If it returns false, model loading is immediately aborted.
         progress_callback_user_data (ctypes.c_void_p): context pointer passed to the progress callback
         kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
@@ -760,16 +760,43 @@ _lib.llama_time_us.argtypes = []
 _lib.llama_time_us.restype = ctypes.c_int64
 
 
-# LLAMA_API int32_t  llama_max_devices(void);
+# LLAMA_API size_t llama_max_devices(void);
 def llama_max_devices() -> int:
     return _lib.llama_max_devices()
 
 
 _lib.llama_max_devices.argtypes = []
-_lib.llama_max_devices.restype = ctypes.c_int32
+_lib.llama_max_devices.restype = ctypes.c_size_t
 
 
-# LLAMA_API bool llama_mmap_supported (void);
+# LLAMA_API bool llama_supports_mmap       (void);
+def llama_supports_mmap() -> bool:
+    return _lib.llama_supports_mmap()
+
+
+_lib.llama_supports_mmap.argtypes = []
+_lib.llama_supports_mmap.restype = c_bool
+
+
+# LLAMA_API bool llama_supports_mlock      (void);
+def llama_supports_mlock() -> bool:
+    return _lib.llama_supports_mlock()
+
+
+_lib.llama_supports_mlock.argtypes = []
+_lib.llama_supports_mlock.restype = c_bool
+
+
+# LLAMA_API bool llama_supports_gpu_offload(void);
+def llama_supports_gpu_offload() -> bool:
+    return _lib.llama_supports_gpu_offload()
+
+
+_lib.llama_supports_gpu_offload.argtypes = []
+_lib.llama_supports_gpu_offload.restype = c_bool
+
+
+# LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");
 def llama_mmap_supported() -> bool:
     return _lib.llama_mmap_supported()
 
@@ -778,7 +805,7 @@ _lib.llama_mmap_supported.argtypes = []
 _lib.llama_mmap_supported.restype = c_bool
 
 
-# LLAMA_API bool llama_mlock_supported(void);
+# LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
 def llama_mlock_supported() -> bool:
     return _lib.llama_mlock_supported()
 
@@ -2174,6 +2201,34 @@ _lib.llama_sample_typical.argtypes = [
 _lib.llama_sample_typical.restype = None
 
 
+# /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772.
+# LLAMA_API void llama_sample_entropy(
+#         struct llama_context * ctx,
+#       llama_token_data_array * candidates_p,
+#                        float   min_temp,
+#                        float   max_temp,
+#                        float   exponent_val);
+def llama_sample_entropy(
+    ctx: llama_context_p,
+    candidates,  # type: _Pointer[llama_token_data_array]
+    min_temp: Union[c_float, float],
+    max_temp: Union[c_float, float],
+    exponent_val: Union[c_float, float],
+):
+    """Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772."""
+    return _lib.llama_sample_entropy(ctx, candidates, min_temp, max_temp, exponent_val)
+
+
+_lib.llama_sample_entropy.argtypes = [
+    llama_context_p,
+    llama_token_data_array_p,
+    c_float,
+    c_float,
+    c_float,
+]
+_lib.llama_sample_entropy.restype = None
+
+
 # LLAMA_API void llama_sample_temp(
 #         struct llama_context * ctx,
 #       llama_token_data_array * candidates,

llama_cpp/llama_types.py

@@ -154,6 +154,7 @@ class ChatCompletionFunctionCallOption(TypedDict):
 
 class ChatCompletionRequestResponseFormat(TypedDict):
     type: Literal["text", "json_object"]
+    schema: NotRequired[JsonType] # https://docs.endpoints.anyscale.com/guides/json_mode/
 
 
 class ChatCompletionRequestMessageContentPartText(TypedDict):

llama_cpp/server/settings.py

@@ -113,8 +113,8 @@ class ModelSettings(BaseSettings):
         description="Enable NUMA support.",
     )
     # Chat Format Params
-    chat_format: str = Field(
-        default="llama-2",
+    chat_format: Optional[str] = Field(
+        default=None,
         description="Chat format to use.",
     )
     clip_model_path: Optional[str] = Field(

tests/test_llama_chat_format.py

@@ -1,10 +1,33 @@
 import json
 
+import jinja2
+
 from llama_cpp import (
     ChatCompletionRequestUserMessage,
 )
+import llama_cpp.llama_types as llama_types
+import llama_cpp.llama_chat_format as llama_chat_format
+
 from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter
 
+def test_mistral_instruct():
+    chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
+    chat_formatter = jinja2.Template(chat_template)
+    messages = [
+        llama_types.ChatCompletionRequestUserMessage(role="user", content="Instruction"),
+        llama_types.ChatCompletionRequestAssistantMessage(role="assistant", content="Model answer"),
+        llama_types.ChatCompletionRequestUserMessage(role="user", content="Follow-up instruction"),
+    ]
+    response = llama_chat_format.format_mistral_instruct(
+        messages=messages,
+    )
+    reference = chat_formatter.render(
+        messages=messages,
+        bos_token="<s>",
+        eos_token="</s>",
+    )
+    assert response.prompt == reference
+
 
 mistral_7b_tokenizer_config = """{
   "add_bos_token": true,

vendor/llama.cpp

@@ -1 +1 @@
-Subproject commit faa3526a1eba458120987ed8269e5616385a76f4
+Subproject commit 5cb04dbc16d1da38c8fdcc0111b40e67d00dd1c3