Python bindings for the Transformer models implemented in C/C++ using GGML library.

Also see ChatDocs

• Supported Models
• Installation
• Usage
  • 🤗 Transformers
  • LangChain
  • GPU
  • GPTQ
• Documentation
• License

pip install ctransformers

It provides a unified interface for all models:

from ctransformers import AutoModelForCausalLM llm = AutoModelForCausalLM.from_pretrained("/path/to/ggml-model.bin", model_type="gpt2") print(llm("AI is going to"))

Run in Google Colab

To stream the output, set stream=True:

for text in llm("AI is going to", stream=True): print(text, end="", flush=True)

You can load models from Hugging Face Hub directly:

llm = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml")

If a model repo has multiple model files (.bin or .gguf files), specify a model file using:

llm = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml", model_file="ggml-model.bin")

Note: This is an experimental feature and may change in the future.

To use it with 🤗 Transformers, create model and tokenizer using:

from ctransformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml", hf=True) tokenizer = AutoTokenizer.from_pretrained(model)

Run in Google Colab

You can use 🤗 Transformers text generation pipeline:

from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) print(pipe("AI is going to", max_new_tokens=256))

You can use 🤗 Transformers generation parameters:

pipe("AI is going to", max_new_tokens=256, do_sample=True, temperature=0.8, repetition_penalty=1.1)

You can use 🤗 Transformers tokenizers:

from ctransformers import AutoModelForCausalLM from transformers import AutoTokenizer model = AutoModelForCausalLM.from_pretrained("marella/gpt-2-ggml", hf=True) # Load model from GGML model repo. tokenizer = AutoTokenizer.from_pretrained("gpt2") # Load tokenizer from original model repo.

It is integrated into LangChain. See LangChain docs.

To run some of the model layers on GPU, set the gpu_layers parameter:

llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-GGML", gpu_layers=50)

Run in Google Colab

Install CUDA libraries using:

pip install ctransformers[cuda]

To enable ROCm support, install the ctransformers package using:

CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers

To enable Metal support, install the ctransformers package using:

CT_METAL=1 pip install ctransformers --no-binary ctransformers

Note: This is an experimental feature and only LLaMA models are supported using ExLlama.

Install additional dependencies using:

pip install ctransformers[gptq]

Load a GPTQ model using:

llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-GPTQ")

Run in Google Colab

If model name or path doesn't contain the word gptq then specify model_type="gptq".

It can also be used with LangChain. Low-level APIs are not fully supported.

Note: Currently only LLaMA, MPT and Falcon models support the context_length parameter.

from_pretrained( model_path_or_repo_id: str, model_type: Optional[str] = None, model_file: Optional[str] = None, config: Optional[ctransformers.hub.AutoConfig] = None, lib: Optional[str] = None, local_files_only: bool = False, revision: Optional[str] = None, hf: bool = False, **kwargs ) → LLM

Loads the language model from a local file or remote repo.

Args:

• model_path_or_repo_id: The path to a model file or directory or the name of a Hugging Face Hub model repo.
• model_type: The model type.
• model_file: The name of the model file in repo or directory.
• config: AutoConfig object.
• lib: The path to a shared library or one of avx2, avx, basic.
• local_files_only: Whether or not to only look at local files (i.e., do not try to download the model).
• revision: The specific model version to use. It can be a branch name, a tag name, or a commit id.
• hf: Whether to create a Hugging Face Transformers model.

Returns: LLM object.

__init__( model_path: str, model_type: Optional[str] = None, config: Optional[ctransformers.llm.Config] = None, lib: Optional[str] = None )

Loads the language model from a local file.

Args:

• model_path: The path to a model file.
• model_type: The model type.
• config: Config object.
• lib: The path to a shared library or one of avx2, avx, basic.

The beginning-of-sequence token.

The config object.

The context length of model.

The input embeddings.

The end-of-sequence token.

The unnormalized log probabilities.

The path to the model file.

The model type.

The padding token.

The number of tokens in vocabulary.

detokenize(tokens: Sequence[int], decode: bool = True) → Union[str, bytes]

Converts a list of tokens to text.

Args:

• tokens: The list of tokens.
• decode: Whether to decode the text as UTF-8 string.

Returns: The combined text of all tokens.

embed( input: Union[str, Sequence[int]], batch_size: Optional[int] = None, threads: Optional[int] = None ) → List[float]

Computes embeddings for a text or list of tokens.

Note: Currently only LLaMA and Falcon models support embeddings.

Args:

• input: The input text or list of tokens to get embeddings for.
• batch_size: The batch size to use for evaluating tokens in a single prompt. Default: 8
• threads: The number of threads to use for evaluating tokens. Default: -1

Returns: The input embeddings.

eval( tokens: Sequence[int], batch_size: Optional[int] = None, threads: Optional[int] = None ) → None

Evaluates a list of tokens.

Args:

• tokens: The list of tokens to evaluate.
• batch_size: The batch size to use for evaluating tokens in a single prompt. Default: 8
• threads: The number of threads to use for evaluating tokens. Default: -1

generate( tokens: Sequence[int], top_k: Optional[int] = None, top_p: Optional[float] = None, temperature: Optional[float] = None, repetition_penalty: Optional[float] = None, last_n_tokens: Optional[int] = None, seed: Optional[int] = None, batch_size: Optional[int] = None, threads: Optional[int] = None, reset: Optional[bool] = None ) → Generator[int, NoneType, NoneType]

Generates new tokens from a list of tokens.

Args:

• tokens: The list of tokens to generate tokens from.
• top_k: The top-k value to use for sampling. Default: 40
• top_p: The top-p value to use for sampling. Default: 0.95
• temperature: The temperature to use for sampling. Default: 0.8
• repetition_penalty: The repetition penalty to use for sampling. Default: 1.1
• last_n_tokens: The number of last tokens to use for repetition penalty. Default: 64
• seed: The seed value to use for sampling tokens. Default: -1
• batch_size: The batch size to use for evaluating tokens in a single prompt. Default: 8
• threads: The number of threads to use for evaluating tokens. Default: -1
• reset: Whether to reset the model state before generating text. Default: True

Returns: The generated tokens.

is_eos_token(token: int) → bool

Checks if a token is an end-of-sequence token.

Args:

• token: The token to check.

Returns: True if the token is an end-of-sequence token else False.

prepare_inputs_for_generation( tokens: Sequence[int], reset: Optional[bool] = None ) → Sequence[int]

Removes input tokens that are evaluated in the past and updates the LLM context.

Args:

• tokens: The list of input tokens.
• reset: Whether to reset the model state before generating text. Default: True

Returns: The list of tokens to evaluate.

reset() → None

Deprecated since 0.2.27.

sample( top_k: Optional[int] = None, top_p: Optional[float] = None, temperature: Optional[float] = None, repetition_penalty: Optional[float] = None, last_n_tokens: Optional[int] = None, seed: Optional[int] = None ) → int

Samples a token from the model.

Args:

• top_k: The top-k value to use for sampling. Default: 40
• top_p: The top-p value to use for sampling. Default: 0.95
• temperature: The temperature to use for sampling. Default: 0.8
• repetition_penalty: The repetition penalty to use for sampling. Default: 1.1
• last_n_tokens: The number of last tokens to use for repetition penalty. Default: 64
• seed: The seed value to use for sampling tokens. Default: -1

Returns: The sampled token.

tokenize(text: str, add_bos_token: Optional[bool] = None) → List[int]

Converts a text into list of tokens.

Args:

• text: The text to tokenize.
• add_bos_token: Whether to add the beginning-of-sequence token.

Returns: The list of tokens.

__call__( prompt: str, max_new_tokens: Optional[int] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, temperature: Optional[float] = None, repetition_penalty: Optional[float] = None, last_n_tokens: Optional[int] = None, seed: Optional[int] = None, batch_size: Optional[int] = None, threads: Optional[int] = None, stop: Optional[Sequence[str]] = None, stream: Optional[bool] = None, reset: Optional[bool] = None ) → Union[str, Generator[str, NoneType, NoneType]]

Generates text from a prompt.

Args:

• prompt: The prompt to generate text from.
• max_new_tokens: The maximum number of new tokens to generate. Default: 256
• top_k: The top-k value to use for sampling. Default: 40
• top_p: The top-p value to use for sampling. Default: 0.95
• temperature: The temperature to use for sampling. Default: 0.8
• repetition_penalty: The repetition penalty to use for sampling. Default: 1.1
• last_n_tokens: The number of last tokens to use for repetition penalty. Default: 64
• seed: The seed value to use for sampling tokens. Default: -1
• batch_size: The batch size to use for evaluating tokens in a single prompt. Default: 8
• threads: The number of threads to use for evaluating tokens. Default: -1
• stop: A list of sequences to stop generation when encountered. Default: None
• stream: Whether to stream the generated text. Default: False
• reset: Whether to reset the model state before generating text. Default: True

Returns: The generated text.

MIT