As you may have noticed in the welcome guide, every interaction with your LLM starts by instantiating a Chat object. In the following sections, we talk about which configuration options it has, and when to use them.

Prompts and responses

The Chat.ask() function is central to NobodyWho. This function sends your message to the LLM, which then starts generating a response.

from nobodywho import Chat, TokenStream
chat = Chat("./model.gguf")
response: TokenStream = chat.ask("Is water wet?")

The return type of ask is a TokenStream. If you want to start reading the response as soon as possible, you can just iterate over the TokenStream. Each token is either an individual word or fragments of a word.

for token in response:
   print(token, end="", flush=True)
print("\n")

If you just want to get the complete response, you can call TokenStream.completed(). This will block until the model is done generating its entire response.

full_response: str = response.completed()

All of your messages and the model's responses are stored in the Chat object, so the next time you call Chat.ask(), it will remember the previous messages.

Chat history

If you want to inspect the messages inside the Chat object, you can use get_chat_history.

msgs: list[dict] = chat.get_chat_history()
print(msgs[0]["content"]) # "Is water wet?"

Similarly, if you want to edit what messages are in the context, you can use set_chat_history:

chat.set_chat_history([{
   "role": "user",
   "content": "What is water?",
   "assets": []
}])

System prompt

A system prompt is a special message put into the chat context, which should guide its overall behavior. Some models ship with a built-in system prompt. If you don't specify a system prompt yourself, NobodyWho will fall back to using the model's default system prompt.

You can specify a system prompt when initializing a Chat:

from nobodywho import Chat
chat = Chat("./model.gguf", system_prompt="You are a mischievous assistant!")

This system_prompt is then persisted until the chat context is reset.

Context

The context is the text window which the LLM currently considers. Specifically this is the number of tokens the LLM keeps in memory for your current conversation. As bigger context size means more computational overhead, it makes sense to constrain it. This can be done with n_ctx setting, again at the time of creation:

chat = Chat("./model.gguf", n_ctx=4096)

The default value is 4096, however this is mainly useful for short and simple conversations. Choosing the right context size is quite important and depends heavily on your use case. A good place to start is to look at your selected models documentation and see what their recommended context size is.

Even with properly selected context size it might happen that you fill up your entire context during a conversation. When this happens, NobodyWho will shrink the context for you. Currently this is done by removing old messages (apart from the system prompt and the first user message) from the chat history, until the size reaches n_ctx / 2. The KV cache is also updated automatically. In the future we plan on adding more advanced methods of context shrinking.

Again, n_ctx is fixed to the Chat instance, so it is currently not possible to change the size after Chat is created. To reset the current context content, just call .reset() with the new system prompt and potentially changed tools.

chat.reset(system_prompt="New system prompt", tools=[])

If you don't want to change the already set defaults (system_prompt, tools), but only reset the context, then go for reset_history.

Sharing model between contexts

There are scenarios where you would like to keep separate chat contexts (e.g. for every user of your app), but have only one model loaded. With plain Chat this is not possible.

For this use case, instead of the path to the .gguf model, you can pass in Model object, which can be shared between multiple Chat instances.

from nobodywho import Chat, Model

model = Model('./model.gguf')
chat1 = Chat(model)
chat2 = Chat(model)
...

NobodyWho will then take care of the separation, such that your chat histories won't collide or interfere with each other, while having only one model loaded.

Asynchronous model loading

Loading a model into memory can take a few seconds - longer if you're using a really large model.

If you want to load the model without blocking execution of your application (e.g. to keep UI responsive), you can load the model asynchronously:

import asyncio
from nobodywho import ChatAsync, Model

async def main():
   model = await Model.load_model_async("./model.gguf")
   chat = ChatAsync(model)

asyncio.run(main())

GPU

Instantiating Model is also useful, when enabling GPU acceleration. This can be done as:

Model('./model.gguf', use_gpu_if_available=True)

So far, NobodyWho relies purely on Vulkan, however support of more architectures is planned (for details check out our issues or join us on Discord).

Template Variables

Chat templates are used internally by models to format conversation history into the expected prompt format. Different models may support different template variables that control specific behaviors. Template variables are boolean flags passed to the chat template that can enable or disable certain features.

Using Template Variables

You can set template variables when creating a chat or modify them on existing instances:

# Set template variables when creating a chat
chat = Chat("./model.gguf", template_variables={"enable_thinking": True})

You can also modify template variables on an existing chat instance:

# Set a single template variable
chat.set_template_variable("enable_thinking", True)

# Set multiple template variables at once
chat.set_template_variables({
    "enable_thinking": True,
    "verbose_mode": False
})

# Get current template variables
variables = chat.get_template_variables()
print(variables)  # {"enable_thinking": True, "verbose_mode": False}

With the next message sent, the updated settings will be propagated to the model.

Example: Qwen3 and Qwen3.5 Reasoning

The Qwen3 and Qwen3.5 model families support the enable_thinking template variable, which controls whether the model should engage in explicit reasoning steps before answering:

# Enable thinking mode for Qwen models
chat = Chat("./model.gguf", template_variables={"enable_thinking": True})
chat.ask("Solve this logic puzzle: ...")

When enable_thinking is enabled, these models will show their reasoning process before providing the final answer.

Model-Specific Variables

Different models may support different template variables depending on their chat template implementation. The available variables and their effects depend entirely on how the model's chat template is designed. Check your model's documentation to see which template variables are supported.

Backward Compatibility

For backward compatibility, the deprecated allow_thinking parameter is still available but internally sets the enable_thinking template variable:

# Deprecated - use template_variables instead
chat = Chat("./model.gguf", allow_thinking=True)
chat.set_allow_thinking(True)