LlamaCppLLM¶
LlamaCppLLM
¶
Bases: LLM
llama.cpp LLM implementation running the Python bindings for the C++ code.
Attributes:
Name | Type | Description |
---|---|---|
model_path |
RuntimeParameter[FilePath]
|
contains the path to the GGUF quantized model, compatible with the
installed version of the |
n_gpu_layers |
RuntimeParameter[int]
|
the number of layers to use for the GPU. Defaults to |
chat_format |
Optional[RuntimeParameter[str]]
|
the chat format to use for the model. Defaults to |
n_ctx |
int
|
the context size to use for the model. Defaults to |
n_batch |
int
|
the prompt processing maximum batch size to use for the model. Defaults to |
seed |
int
|
random seed to use for the generation. Defaults to |
verbose |
RuntimeParameter[bool]
|
whether to print verbose output. Defaults to |
structured_output |
Optional[RuntimeParameter[OutlinesStructuredOutputType]]
|
a dictionary containing the structured output configuration or if more
fine-grained control is needed, an instance of |
extra_kwargs |
Optional[RuntimeParameter[Dict[str, Any]]]
|
additional dictionary of keyword arguments that will be passed to the
|
_model |
Optional[Llama]
|
the Llama model instance. This attribute is meant to be used internally and
should not be accessed directly. It will be set in the |
Runtime parameters
model_path
: the path to the GGUF quantized model.n_gpu_layers
: the number of layers to use for the GPU. Defaults to-1
.chat_format
: the chat format to use for the model. Defaults toNone
.verbose
: whether to print verbose output. Defaults toFalse
.extra_kwargs
: additional dictionary of keyword arguments that will be passed to theLlama
class ofllama_cpp
library. Defaults to{}
.
References
Examples:
Generate text:
```python
from pathlib import Path
from distilabel.llms import LlamaCppLLM
# You can follow along this example downloading the following model running the following
# command in the terminal, that will download the model to the `Downloads` folder:
# curl -L -o ~/Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q4_K_M.gguf
model_path = "Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf"
llm = LlamaCppLLM(
model_path=str(Path.home() / model_path),
n_gpu_layers=-1, # To use the GPU if available
n_ctx=1024, # Set the context size
)
llm.load()
# Call the model
output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
```
Generate structured data:
```python
from pathlib import Path
from distilabel.llms import LlamaCppLLM
model_path = "Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf"
class User(BaseModel):
name: str
last_name: str
id: int
llm = LlamaCppLLM(
model_path=str(Path.home() / model_path), # type: ignore
n_gpu_layers=-1,
n_ctx=1024,
structured_output={"format": "json", "schema": Character},
)
llm.load()
# Call the model
output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
```
Source code in src/distilabel/llms/llamacpp.py
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|
model_name: str
property
¶
Returns the model name used for the LLM.
generate(inputs, num_generations=1, max_new_tokens=128, frequency_penalty=0.0, presence_penalty=0.0, temperature=1.0, top_p=1.0, extra_generation_kwargs=None)
¶
Generates num_generations
responses for the given input using the Llama model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs |
List[FormattedInput]
|
a list of inputs in chat format to generate responses for. |
required |
num_generations |
int
|
the number of generations to create per input. Defaults to
|
1
|
max_new_tokens |
int
|
the maximum number of new tokens that the model will generate.
Defaults to |
128
|
frequency_penalty |
float
|
the repetition penalty to use for the generation. Defaults
to |
0.0
|
presence_penalty |
float
|
the presence penalty to use for the generation. Defaults to
|
0.0
|
temperature |
float
|
the temperature to use for the generation. Defaults to |
1.0
|
top_p |
float
|
the top-p value to use for the generation. Defaults to |
1.0
|
extra_generation_kwargs |
Optional[Dict[str, Any]]
|
dictionary with additional arguments to be passed to
the |
None
|
Returns:
Type | Description |
---|---|
List[GenerateOutput]
|
A list of lists of strings containing the generated responses for each input. |
Source code in src/distilabel/llms/llamacpp.py
load()
¶
Loads the Llama
model from the model_path
.