Add serialize methods for ChatMessage + ChatFeed

[ahuang11]

I'm trying to make ChatInterface easy to use to fine tune LLMs. The first step is easy exporting of it, ready to use for transformers.

Kind of addresses #5567, but specifically for use with fine tuning transformers https://huggingface.co/docs/transformers/main/chat_templating, not ChatInterface.

I was initially thinking of combining these it like in the ChatBox export method #5178, but decided against it since the logic got long.

There's a lot of ways to serialize the widgets / panes / etc, so I added format_func as an escape hatch if users don't agree with the default.

Would like some early thoughts before I add tests.

import panel as pn
import numpy as np
import pandas as pd

pn.extension()

msg = pn.chat.ChatMessage(
    pn.Row(
        pn.widgets.FloatSlider(value=10),
        pn.widgets.Checkbox(name="Check me!"),
        pn.pane.HTML("Hello World!"),
        pn.pane.DataFrame(
            pd.DataFrame(np.random.randn(10, 3), columns=list("ABC"))
        ),
    ),
)
print(str(msg))

• Add tests
• Use user from ChatInterface in user_names

[codecov]

Codecov Report

Attention: 4 lines in your changes are missing coverage. Please review.

Comparison is base (7eb9c71) 84.00% compared to head (b7ad0d5) 82.33%.
Report is 5 commits behind head on main.

Files	Patch %	Lines
panel/chat/interface.py	40.00%	3 Missing ⚠️
panel/chat/message.py	96.87%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #5764      +/-   ##
==========================================
- Coverage   84.00%   82.33%   -1.67%     
==========================================
  Files         290      290              
  Lines       42391    42622     +231     
==========================================
- Hits        35609    35093     -516     
- Misses       6782     7529     +747     

Flag	Coverage Δ
ui-tests	38.33% <24.00%> (-2.57%)	⬇️
unitexamples-tests	72.27% <97.71%> (-0.09%)	⬇️

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[ahuang11]

Just tried this out and it works well.

import panel as pn
from transformers import AutoTokenizer
from ctransformers import AutoConfig, AutoModelForCausalLM, Config

pn.extension(design="material")

BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.1"
QUANTIZED_MODEL = "TheBloke/Mistral-7B-Instruct-v0.1-GGUF"
QUANTIZED_FILE = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"


async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
    conversation = instance.serialize_for_transformers()
    formatted_conversation = tokenizer.apply_chat_template(conversation, tokenize=False)
    response = llm(formatted_conversation, stream=True)
    message = ""
    for token in response:
        message += token
        yield message

config = AutoConfig(
    config=Config(
        temperature=0.5, max_new_tokens=2048, context_length=2048, gpu_layers=1
    ),
)
llm = AutoModelForCausalLM.from_pretrained(
    QUANTIZED_MODEL,
    model_file=QUANTIZED_FILE,
    config=config,
)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

chat_interface = pn.chat.ChatInterface(
    callback=callback,
    show_send=False,
    show_rerun=False,
    show_undo=False,
    show_clear=False,
    show_button_name=False,
    callback_exception="verbose"
)
pn.template.FastListTemplate(
    main=[chat_interface],
    sidebar_width=400,
).show()