A library helps to communicate with all kinds of LLMs consistently.
Model | State | Note |
---|---|---|
ChatGPT | Done ✅ | |
Llama-2 | Done ✅ | |
CodeLlama | Done ✅ | |
ChatGLM2 | Done ✅ | |
ChatGLM3 | WIP ⏳ | |
Claude-2 | RoadMap 📋 | issue#7 |
Falcon | RoadMap 📋 | issue#8 |
StableLM | RoadMap 📋 | issue#11 |
Baichuan2 | RoadMap 📋 | issue#34 |
... | ... | ... |
We're also planning to support different inference backends as below:
backend | State | Note |
---|---|---|
huggingface | Done ✅ | Default |
vLLM | WIP ⏳ | issue#32 |
... | ... | ... |
pip install llmlite==0.0.9
from llmlite.apis import ChatLLM, ChatMessage
chat = ChatLLM(
model_name_or_path="meta-llama/Llama-2-7b-chat-hf", # required
task="text-generation", # optional, default to 'text-generation'
)
result = chat.completion(
messages=[
ChatMessage(role="system", content="You're a honest assistant."),
ChatMessage(role="user", content="There's a llama in my garden, what should I do?"),
]
)
# Output: Oh my goodness, a llama in your garden?! 😱 That's quite a surprise! 😅 As an honest assistant, I must inform you that llamas are not typically known for their gardening skills, so it's possible that the llama in your garden may have wandered there accidentally or is seeking shelter. 🐮 ...
llmlite
also supports other parameters like temperature
, max_length
, do_sample
, top_k
, top_p
to help control the length, randomness and diversity of the generated text.
See examples for reference.
You can use llmlite
to help you generate full prompts, for instance:
from llmlite.apis import ChatMessage, LlamaChat
messages = [
ChatMessage(role="system", content="You're a honest assistant."),
ChatMessage(role="user", content="There's a llama in my garden, what should I do?"),
]
LlamaChat.prompt(messages)
# Output:
# <s>[INST] <<SYS>>
# You're a honest assistant.
# <</SYS>>
# There's a llama in my garden, what should I do? [/INST]
Set the env variable LOG_LEVEL
for log configuration, default to INFO
, others like DEBUG, INFO, WARNING etc..
- Adapter support
- Quantization
- Streaming
🚀 All kinds of contributions are welcomed ! Please follow Contributing.
🎉 Thanks to all these contributors.