ctransformers 0.2.27

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

CTransformers

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

Supported Models

Models	Model Type	CUDA	Metal
GPT-2	gpt2
GPT-J, GPT4All-J	gptj
GPT-NeoX, StableLM	gpt_neox
Falcon	falcon	✅
LLaMA, LLaMA 2	llama	✅	✅
MPT	mpt	✅
StarCoder, StarChat	gpt_bigcode	✅
Dolly V2	dolly-v2
Replit	replit

Installation

pip install ctransformers

Usage

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")

🤗 Transformers

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.

LangChain

It is integrated into LangChain. See LangChain docs.

GPU

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

CUDA

Install CUDA libraries using:

pip install ctransformers[cuda]

ROCm

To enable ROCm support, install the ctransformers package using:

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

Metal

To enable Metal support, install the ctransformers package using:

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

GPTQ

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.

Documentation

Config

Parameter	Type	Description	Default
top_k	int	The top-k value to use for sampling.	40
top_p	float	The top-p value to use for sampling.	0.95
temperature	float	The temperature to use for sampling.	0.8
repetition_penalty	float	The repetition penalty to use for sampling.	1.1
last_n_tokens	int	The number of last tokens to use for repetition penalty.	64
seed	int	The seed value to use for sampling tokens.	-1
max_new_tokens	int	The maximum number of new tokens to generate.	256
stop	List[str]	A list of sequences to stop generation when encountered.	None
stream	bool	Whether to stream the generated text.	False
reset	bool	Whether to reset the model state before generating text.	True
batch_size	int	The batch size to use for evaluating tokens in a single prompt.	8
threads	int	The number of threads to use for evaluating tokens.	-1
context_length	int	The maximum context length to use.	-1
gpu_layers	int	The number of layers to run on GPU.	0

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

class AutoModelForCausalLM

---

classmethod AutoModelForCausalLM.from_pretrained

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.

class LLM

method LLM.__init__

__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.

---

property LLM.bos_token_id

The beginning-of-sequence token.

---

property LLM.config

The config object.

---

property LLM.context_length

The context length of model.

---

property LLM.embeddings

The input embeddings.

---

property LLM.eos_token_id

The end-of-sequence token.

---

property LLM.logits

The unnormalized log probabilities.

---

property LLM.model_path

The path to the model file.

---

property LLM.model_type

The model type.

---

property LLM.pad_token_id

The padding token.

---

property LLM.vocab_size

The number of tokens in vocabulary.

---

method LLM.detokenize

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.

---

method LLM.embed

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.

---

method LLM.eval

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

---

method LLM.generate

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.

---

method LLM.is_eos_token

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.

---

method LLM.prepare_inputs_for_generation

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.

---

method LLM.reset

reset() → None

Deprecated since 0.2.27.

---

method LLM.sample

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.

---

method LLM.tokenize

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.

---

method LLM.__call__

__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.

License

MIT