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19
CHANGELOG.md
View file

@ -7,25 +7,6 @@ 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

9
Makefile
View file

@ -27,15 +27,6 @@ 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

90
README.md
View file

@ -23,6 +23,9 @@ This package provides:
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:
@ -35,6 +38,7 @@ 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.
@ -67,7 +71,7 @@ CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-
#### cuBLAS
To install with cuBLAS, set the `LLAMA_CUBLAS=on` environment variable before installing:
To install with cuBLAS, set the `LLAMA_CUBLAS=1` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
@ -83,7 +87,7 @@ CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
#### CLBlast
To install with CLBlast, set the `LLAMA_CLBLAST=on` environment variable before installing:
To install with CLBlast, set the `LLAMA_CLBLAST=1` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
@ -97,30 +101,6 @@ 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:
@ -236,59 +216,6 @@ 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.
@ -296,6 +223,7 @@ 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")
@ -343,6 +271,7 @@ 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.
@ -388,6 +317,7 @@ 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.
@ -435,7 +365,6 @@ 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)
## Low-level API
@ -464,6 +393,7 @@ 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/).

2
examples/high_level_api/fastapi_server.py
View file

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

2
llama_cpp/__init__.py
View file

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

4
llama_cpp/_internals.py
View file

@ -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 + 1
buffer_size = nbytes
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 + 1
buffer_size = nbytes
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")

37
llama_cpp/llama.py
View file

@ -87,7 +87,7 @@ class Llama:
# Backend Params
numa: bool = False,
# Chat Format Params
chat_format: Optional[str] = None,
chat_format: str = "llama-2",
chat_handler: Optional[llama_chat_format.LlamaChatCompletionHandler] = None,
# Misc
verbose: bool = True,
@ -343,41 +343,6 @@ 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

82
llama_cpp/llama_chat_format.py
View file

@ -14,20 +14,6 @@ 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.
@ -132,6 +118,7 @@ def register_chat_completion_handler(name: str):
### Chat Formatter ###
@dataclasses.dataclass
class ChatFormatterResponse:
"""Dataclass that stores completion parameters for a given chat format and
@ -185,17 +172,16 @@ class Jinja2ChatFormatter(ChatFormatter):
messages: List[llama_types.ChatCompletionRequestMessage],
**kwargs: Any,
) -> ChatFormatterResponse:
def raise_exception(message: str):
raise ValueError(message)
if self.add_generation_prompt:
messages = [
*messages,
llama_types.ChatCompletionRequestAssistantMessage(
role="assistant", content=""
),
]
prompt = self._environment.render(
messages=messages,
eos_token=self.eos_token,
bos_token=self.bos_token,
raise_exception=raise_exception,
add_generation_prompt=self.add_generation_prompt
messages=messages, eos_token=self.eos_token, bos_token=self.bos_token
)
return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token])
def to_chat_handler(self) -> LlamaChatCompletionHandler:
@ -332,13 +318,6 @@ def chat_formatter_to_chat_completion_handler(
stop = stop + rstop
if response_format is not None and response_format["type"] == "json_object":
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(
@ -454,20 +433,7 @@ 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(
@ -904,24 +870,6 @@ 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],
@ -956,6 +904,7 @@ 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")
@ -977,7 +926,6 @@ 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(
@ -1486,14 +1434,10 @@ class Llava15ChatHandler:
prompt = llama.input_ids[: llama.n_tokens].tolist()
if response_format is not None and response_format["type"] == "json_object":
try:
# create grammar from json schema
if "schema" in response_format:
grammar = llama_grammar.LlamaGrammar.from_json_schema(
json.dumps(response_format["schema"])
with suppress_stdout_stderr(disable=self.verbose):
grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF
)
except Exception as e:
grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
return _convert_completion_to_chat(
llama.create_completion(

75
llama_cpp/llama_cpp.py
View file

@ -93,12 +93,14 @@ 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_size_t
_lib.llama_max_devices.restype = ctypes.c_int32
LLAMA_MAX_DEVICES = _lib.llama_max_devices()
@ -187,7 +189,6 @@ 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
# };
@ -212,7 +213,6 @@ 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
@ -760,43 +760,16 @@ _lib.llama_time_us.argtypes = []
_lib.llama_time_us.restype = ctypes.c_int64
# LLAMA_API size_t llama_max_devices(void);
# LLAMA_API int32_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_size_t
_lib.llama_max_devices.restype = ctypes.c_int32
# 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");
# LLAMA_API bool llama_mmap_supported (void);
def llama_mmap_supported() -> bool:
return _lib.llama_mmap_supported()
@ -805,7 +778,7 @@ _lib.llama_mmap_supported.argtypes = []
_lib.llama_mmap_supported.restype = c_bool
# LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
# LLAMA_API bool llama_mlock_supported(void);
def llama_mlock_supported() -> bool:
return _lib.llama_mlock_supported()
@ -2201,34 +2174,6 @@ _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,

1
llama_cpp/llama_types.py
View file

@ -154,7 +154,6 @@ 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):

4
llama_cpp/server/settings.py
View file

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

23
tests/test_llama_chat_format.py
View file

@ -1,33 +1,10 @@
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,

2
vendor/llama.cpp vendored

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