| Name | pyllamacpp JSON |
| Version |
2.4.3
JSON |
| download |
| home_page | |
| Summary | Python bindings for llama.cpp |
| upload_time | 2024-02-28 23:04:52 |
| maintainer | |
| docs_url | None |
| author | Abdeladim Sadiki |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
|
| VCS |
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| requirements |
No requirements were recorded.
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|
# PyLLaMACpp
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/pyllamacpp/)
[](https://pepy.tech/project/pyllamacpp)
<a target="_blank" href="https://colab.research.google.com/github/abdeladim-s/pyllamacpp/blob/main/examples/PyLLaMACpp.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
* Python bindings for [llama.cpp](https://github.com/ggerganov/llama.cpp).
* If you are looking to run **Falcon** models, take a look at the [ggllm branch](https://github.com/abdeladim-s/pyllamacpp/tree/ggllm.cpp).
<p align="center">
<img src="./docs/demo.gif">
</p>
For those who don't know, `llama.cpp` is a port of Facebook's LLaMA model in pure C/C++:
<blockquote>
- Without dependencies
- Apple silicon first-class citizen - optimized via ARM NEON
- AVX2 support for x86 architectures
- Mixed F16 / F32 precision
- 4-bit quantization support
- Runs on the CPU
</blockquote>
# Table of contents
<!-- TOC -->
* [Installation](#installation)
* [CLI](#cli-)
* [Tutorial](#tutorial)
* [Quick start](#quick-start)
* [Interactive Dialogue](#interactive-dialogue)
* [Attribute a persona to the language model](#attribute-a-persona-to-the-language-model)
* [Example usage with langchain](#example-usage-with-langchain)
* [Supported models](#supported-models)
* [Advanced usage](#advanced-usage)
* [API reference](#api-reference)
* [FAQs](#faqs)
* [Discussions and contributions](#discussions-and-contributions)
* [License](#license)
<!-- TOC -->
# Installation
1. The easy way is to install the prebuilt wheels
```bash
pip install pyllamacpp
```
However, the compilation process of `llama.cpp` is taking into account the architecture of the target `CPU`,
so you might need to build it from source:
```shell
pip install git+https://github.com/abdeladim-s/pyllamacpp.git
```
:warning: **Note**
[This PR](https://github.com/ggerganov/llama.cpp/pull/1405) introduced some breaking changes.
If you want to use older models, use version `2.2.0`:
```bash
pip install pyllamacpp==2.2.0
```
# CLI
You can run the following simple command line interface to test the package once it is installed:
```shell
pyllamacpp path/to/model.bin
```
```shell
pyllamacpp -h
usage: pyllamacpp [-h] [--n_ctx N_CTX] [--n_parts N_PARTS] [--seed SEED] [--f16_kv F16_KV] [--logits_all LOGITS_ALL]
[--vocab_only VOCAB_ONLY] [--use_mlock USE_MLOCK] [--embedding EMBEDDING] [--n_predict N_PREDICT] [--n_threads N_THREADS]
[--repeat_last_n REPEAT_LAST_N] [--top_k TOP_K] [--top_p TOP_P] [--temp TEMP] [--repeat_penalty REPEAT_PENALTY]
[--n_batch N_BATCH]
model
This is like a chatbot, You can start the conversation with `Hi, can you help me ?` Pay attention though that it may hallucinate!
positional arguments:
model The path of the model file
options:
-h, --help show this help message and exit
--n_ctx N_CTX text context
--n_parts N_PARTS
--seed SEED RNG seed
--f16_kv F16_KV use fp16 for KV cache
--logits_all LOGITS_ALL
the llama_eval() call computes all logits, not just the last one
--vocab_only VOCAB_ONLY
only load the vocabulary, no weights
--use_mlock USE_MLOCK
force system to keep model in RAM
--embedding EMBEDDING
embedding mode only
--n_predict N_PREDICT
Number of tokens to predict
--n_threads N_THREADS
Number of threads
--repeat_last_n REPEAT_LAST_N
Last n tokens to penalize
--top_k TOP_K top_k
--top_p TOP_P top_p
--temp TEMP temp
--repeat_penalty REPEAT_PENALTY
repeat_penalty
--n_batch N_BATCH batch size for prompt processing
```
# Tutorial
### Quick start
```python
from pyllamacpp.model import Model
model = Model(model_path='/path/to/model.bin')
for token in model.generate("Tell me a joke ?\n"):
print(token, end='', flush=True)
```
### Interactive Dialogue
You can set up an interactive dialogue by simply keeping the `model` variable alive:
```python
from pyllamacpp.model import Model
model = Model(model_path='/path/to/model.bin')
while True:
try:
prompt = input("You: ", flush=True)
if prompt == '':
continue
print(f"AI:", end='')
for token in model.generate(prompt):
print(f"{token}", end='', flush=True)
print()
except KeyboardInterrupt:
break
```
### Attribute a persona to the language model
The following is an example showing how to _"attribute a persona to the language model"_ :
```python
from pyllamacpp.model import Model
prompt_context = """Act as Bob. Bob is helpful, kind, honest,
and never fails to answer the User's requests immediately and with precision.
User: Nice to meet you Bob!
Bob: Welcome! I'm here to assist you with anything you need. What can I do for you today?
"""
prompt_prefix = "\nUser:"
prompt_suffix = "\nBob:"
model = Model(model_path='/path/to/model.bin',
n_ctx=512,
prompt_context=prompt_context,
prompt_prefix=prompt_prefix,
prompt_suffix=prompt_suffix)
while True:
try:
prompt = input("User: ")
if prompt == '':
continue
print(f"Bob: ", end='')
for token in model.generate(prompt,
antiprompt='User:',
n_threads=6,
n_batch=1024,
n_predict=256,
n_keep=48,
repeat_penalty=1.0, ):
print(f"{token}", end='', flush=True)
print()
except KeyboardInterrupt:
break
```
### Example usage with [langchain](https://github.com/langchain-ai/langchain)
```python
from pyllamacpp.langchain_llm import PyllamacppLLM
llm = PyllamacppLLM(
model="path/to/ggml/model",
temp=0.75,
n_predict=50,
top_p=1,
top_k=40
)
template = "\n\n##Instruction:\n:{question}\n\n##Response:\n"
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What are large language models?"
answer = llm_chain.run(question)
print(answer)
```
# Supported models
All models supported by `llama.cpp` should be supported basically:
<blockquote>
**Supported models:**
- [X] LLaMA 🦙
- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
</blockquote>
# Advanced usage
For advanced users, you can access the [llama.cpp C-API](https://github.com/ggerganov/llama.cpp/blob/master/llama.h) functions directly to make your own logic.
All functions from `llama.h` are exposed with the binding module [`_pyllamacpp`](https://abdeladim-s.github.io/pyllamacpp/#_pyllamacpp).
# API reference
You can check the [API reference documentation](https://abdeladim-s.github.io/pyllamacpp/) for more details.
# FAQs
* [How to build pyllamacpp without AVX2 or FMA.](https://github.com/nomic-ai/pygpt4all/issues/71)
* [pyllamacpp does not support M1 chips MacBook](https://github.com/nomic-ai/pygpt4all/issues/57#issuecomment-1519197837)
* [ImportError: DLL failed while importing _pyllamacpp](https://github.com/nomic-ai/pygpt4all/issues/53#issuecomment-1529772010)
# Discussions and contributions
If you find any bug, please open an [issue](https://github.com/abdeladim-s/pyllamacpp/issues).
If you have any feedback, or you want to share how you are using this project, feel free to use the [Discussions](https://github.com/abdeladim-s/pyllamacpp/discussions) and open a new topic.
# License
This project is licensed under the same license as [llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/LICENSE) (MIT [License](./LICENSE)).
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"description": "# PyLLaMACpp\n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/pyllamacpp/)\n[](https://pepy.tech/project/pyllamacpp)\n<a target=\"_blank\" href=\"https://colab.research.google.com/github/abdeladim-s/pyllamacpp/blob/main/examples/PyLLaMACpp.ipynb\">\n <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n</a>\n\n* Python bindings for [llama.cpp](https://github.com/ggerganov/llama.cpp).\n* If you are looking to run **Falcon** models, take a look at the [ggllm branch](https://github.com/abdeladim-s/pyllamacpp/tree/ggllm.cpp).\n\n<p align=\"center\">\n <img src=\"./docs/demo.gif\">\n</p>\n\n\nFor those who don't know, `llama.cpp` is a port of Facebook's LLaMA model in pure C/C++:\n\n<blockquote>\n\n- Without dependencies\n- Apple silicon first-class citizen - optimized via ARM NEON\n- AVX2 support for x86 architectures\n- Mixed F16 / F32 precision\n- 4-bit quantization support\n- Runs on the CPU\n\n</blockquote>\n\n# Table of contents\n<!-- TOC -->\n* [Installation](#installation)\n* [CLI](#cli-)\n* [Tutorial](#tutorial)\n * [Quick start](#quick-start)\n * [Interactive Dialogue](#interactive-dialogue)\n * [Attribute a persona to the language model](#attribute-a-persona-to-the-language-model)\n * [Example usage with langchain](#example-usage-with-langchain)\n* [Supported models](#supported-models)\n* [Advanced usage](#advanced-usage)\n* [API reference](#api-reference)\n* [FAQs](#faqs)\n* [Discussions and contributions](#discussions-and-contributions)\n* [License](#license)\n<!-- TOC -->\n\n# Installation\n1. The easy way is to install the prebuilt wheels\n```bash\npip install pyllamacpp\n```\n\nHowever, the compilation process of `llama.cpp` is taking into account the architecture of the target `CPU`, \nso you might need to build it from source:\n\n```shell\npip install git+https://github.com/abdeladim-s/pyllamacpp.git\n```\n\n:warning: **Note**\n\n[This PR](https://github.com/ggerganov/llama.cpp/pull/1405) introduced some breaking changes.\nIf you want to use older models, use version `2.2.0`:\n```bash\npip install pyllamacpp==2.2.0\n```\n\n# CLI \n\nYou can run the following simple command line interface to test the package once it is installed:\n\n```shell\npyllamacpp path/to/model.bin\n```\n\n```shell\npyllamacpp -h\n\nusage: pyllamacpp [-h] [--n_ctx N_CTX] [--n_parts N_PARTS] [--seed SEED] [--f16_kv F16_KV] [--logits_all LOGITS_ALL]\n [--vocab_only VOCAB_ONLY] [--use_mlock USE_MLOCK] [--embedding EMBEDDING] [--n_predict N_PREDICT] [--n_threads N_THREADS]\n [--repeat_last_n REPEAT_LAST_N] [--top_k TOP_K] [--top_p TOP_P] [--temp TEMP] [--repeat_penalty REPEAT_PENALTY]\n [--n_batch N_BATCH]\n model\n\nThis is like a chatbot, You can start the conversation with `Hi, can you help me ?` Pay attention though that it may hallucinate!\n\npositional arguments:\n model The path of the model file\n\noptions:\n -h, --help show this help message and exit\n --n_ctx N_CTX text context\n --n_parts N_PARTS\n --seed SEED RNG seed\n --f16_kv F16_KV use fp16 for KV cache\n --logits_all LOGITS_ALL\n the llama_eval() call computes all logits, not just the last one\n --vocab_only VOCAB_ONLY\n only load the vocabulary, no weights\n --use_mlock USE_MLOCK\n force system to keep model in RAM\n --embedding EMBEDDING\n embedding mode only\n --n_predict N_PREDICT\n Number of tokens to predict\n --n_threads N_THREADS\n Number of threads\n --repeat_last_n REPEAT_LAST_N\n Last n tokens to penalize\n --top_k TOP_K top_k\n --top_p TOP_P top_p\n --temp TEMP temp\n --repeat_penalty REPEAT_PENALTY\n repeat_penalty\n --n_batch N_BATCH batch size for prompt processing\n```\n\n# Tutorial\n\n### Quick start\n\n```python\nfrom pyllamacpp.model import Model\n\nmodel = Model(model_path='/path/to/model.bin')\nfor token in model.generate(\"Tell me a joke ?\\n\"):\n print(token, end='', flush=True)\n```\n\n### Interactive Dialogue\nYou can set up an interactive dialogue by simply keeping the `model` variable alive:\n\n```python\nfrom pyllamacpp.model import Model\n\nmodel = Model(model_path='/path/to/model.bin')\nwhile True:\n try:\n prompt = input(\"You: \", flush=True)\n if prompt == '':\n continue\n print(f\"AI:\", end='')\n for token in model.generate(prompt):\n print(f\"{token}\", end='', flush=True)\n print()\n except KeyboardInterrupt:\n break\n```\n### Attribute a persona to the language model\n\nThe following is an example showing how to _\"attribute a persona to the language model\"_ :\n\n```python\nfrom pyllamacpp.model import Model\n\nprompt_context = \"\"\"Act as Bob. Bob is helpful, kind, honest,\nand never fails to answer the User's requests immediately and with precision. \n\nUser: Nice to meet you Bob!\nBob: Welcome! I'm here to assist you with anything you need. What can I do for you today?\n\"\"\"\n\nprompt_prefix = \"\\nUser:\"\nprompt_suffix = \"\\nBob:\"\n\nmodel = Model(model_path='/path/to/model.bin',\n n_ctx=512,\n prompt_context=prompt_context,\n prompt_prefix=prompt_prefix,\n prompt_suffix=prompt_suffix)\n\nwhile True:\n try:\n prompt = input(\"User: \")\n if prompt == '':\n continue\n print(f\"Bob: \", end='')\n for token in model.generate(prompt,\n antiprompt='User:',\n n_threads=6,\n n_batch=1024,\n n_predict=256,\n n_keep=48,\n repeat_penalty=1.0, ):\n print(f\"{token}\", end='', flush=True)\n print()\n except KeyboardInterrupt:\n break\n```\n\n### Example usage with [langchain](https://github.com/langchain-ai/langchain)\n\n```python\nfrom pyllamacpp.langchain_llm import PyllamacppLLM\n\nllm = PyllamacppLLM(\n model=\"path/to/ggml/model\",\n temp=0.75,\n n_predict=50,\n top_p=1,\n top_k=40\n)\n\ntemplate = \"\\n\\n##Instruction:\\n:{question}\\n\\n##Response:\\n\"\n\nprompt = PromptTemplate(template=template, input_variables=[\"question\"])\n\nllm_chain = LLMChain(prompt=prompt, llm=llm)\n\nquestion = \"What are large language models?\"\nanswer = llm_chain.run(question)\nprint(answer)\n```\n\n# Supported models\nAll models supported by `llama.cpp` should be supported basically:\n\n<blockquote>\n\n**Supported models:**\n\n- [X] LLaMA \ud83e\udd99\n- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)\n- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)\n- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)\n- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)\n- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)\n- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)\n- [X] [OpenBuddy \ud83d\udc36 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)\n- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)\n- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)\n\n</blockquote>\n\n# Advanced usage\nFor advanced users, you can access the [llama.cpp C-API](https://github.com/ggerganov/llama.cpp/blob/master/llama.h) functions directly to make your own logic.\nAll functions from `llama.h` are exposed with the binding module [`_pyllamacpp`](https://abdeladim-s.github.io/pyllamacpp/#_pyllamacpp).\n\n# API reference\nYou can check the [API reference documentation](https://abdeladim-s.github.io/pyllamacpp/) for more details.\n\n# FAQs\n* [How to build pyllamacpp without AVX2 or FMA.](https://github.com/nomic-ai/pygpt4all/issues/71)\n* [pyllamacpp does not support M1 chips MacBook](https://github.com/nomic-ai/pygpt4all/issues/57#issuecomment-1519197837)\n* [ImportError: DLL failed while importing _pyllamacpp](https://github.com/nomic-ai/pygpt4all/issues/53#issuecomment-1529772010)\n\n# Discussions and contributions\nIf you find any bug, please open an [issue](https://github.com/abdeladim-s/pyllamacpp/issues).\n\nIf you have any feedback, or you want to share how you are using this project, feel free to use the [Discussions](https://github.com/abdeladim-s/pyllamacpp/discussions) and open a new topic.\n\n# License\n\nThis project is licensed under the same license as [llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/LICENSE) (MIT [License](./LICENSE)).\n\n",
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