training only 2000/5000 complete
The following bitsandbytes quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
PEFT 0.6.0.dev0
To load start a jupyter notebook, here it is all in 2 parts
!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git
!pip install -q -U gradio
!pip install -q -U sentencepiece
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
model_name = "TheBloke/CodeLlama-34B-Instruct-fp16"
adapters_name = 'nisten/bigdoc-c34b-python-v1'
print(f"Starting to load the model {model_name} into memory")
m = AutoModelForCausalLM.from_pretrained(
model_name,
#load_in_4bit=True, #19GB in 4bit, 38GB with load_in_8bit, 67GB in full f16 if you just delete this line
torch_dtype=torch.bfloat16,
device_map={"": 0}
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()
tok = AutoTokenizer.from_pretrained(model_name)
eos_token_id = tok.convert_tokens_to_ids('/s')
tok.eos_token = '/s'
tok.pad_token = tok.eos_token
tok.padding_side = 'right'
tok.eos_token_id = eos_token_id
stop_token_ids = eos_token_id
print(f"Successfully loaded the model {model_name} into memory")
Gradio the UI
#should all work in one click
import datetime
import os
from threading import Event, Thread
from uuid import uuid4
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import gradio as gr
import requests
max_new_tokens = 2369
start_message = """A chat between a chill human asking ( Question: ) and an AI doctor ( Answer: ). The doctor answers in helpful, detailed, and exhaustively nerdy extensive answers to the user's every medical Question:"""
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
if isinstance(stop_token_ids, (list, torch.Tensor)):
for stop_id in stop_token_ids:
if stop_id in input_ids[0]:
return True
else: # Assumes scalar
if input_ids[0][-1] == stop_token_ids:
return True
return False
def convert_history_to_text(history):
text = start_message + "".join(
[
"".join(
[
f" Question: {item[0]}\n",
f"\n\n Answer: {item[1]}\n",
]
)
for item in history[:-1]
]
)
text += "".join(
[
"".join(
[
f" Question: {history[-1][0]}\n",
f"\n\n Answer: {history[-1][1]}\n",
]
)
]
)
return text
def log_conversation(conversation_id, history, messages, generate_kwargs):
logging_url = os.getenv("LOGGING_URL", None)
if logging_url is None:
return
timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
data = {
"conversation_id": conversation_id,
"timestamp": timestamp,
"history": history,
"messages": messages,
"generate_kwargs": generate_kwargs,
}
try:
requests.post(logging_url, json=data)
except requests.exceptions.RequestException as e:
print(f"Error logging conversation: {e}")
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
print(f"history: {history}")
# Initialize a StopOnTokens object
stop = StopOnTokens()
# Construct the input message string for the model by concatenating the current system message and conversation history
messages = convert_history_to_text(history)
# Tokenize the messages string
input_ids = tok(messages, return_tensors="pt").input_ids
input_ids = input_ids.to(m.device)
streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=temperature > 0.0,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
streamer=streamer,
stopping_criteria=StoppingCriteriaList([stop]),
)
stream_complete = Event()
def generate_and_signal_complete():
m.generate(**generate_kwargs)
stream_complete.set()
def log_after_stream_complete():
stream_complete.wait()
log_conversation(
conversation_id,
history,
messages,
{
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
},
)
t1 = Thread(target=generate_and_signal_complete)
t1.start()
t2 = Thread(target=log_after_stream_complete)
t2.start()
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
partial_text += new_text
history[-1][1] = partial_text
yield history
def get_uuid():
return str(uuid4())
with gr.Blocks(
theme=gr.themes.Soft(),
css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
conversation_id = gr.State(get_uuid)
gr.Markdown(
"""<h1><center>Nisten's 34b Doctor v1</center></h1>
"""
)
chatbot = gr.Chatbot().style(height=969)
with gr.Row():
with gr.Column():
msg = gr.Textbox(
label="Chat Message Box",
placeholder="Chat Message Box",
show_label=False,
).style(container=False)
with gr.Column():
with gr.Row():
submit = gr.Button("Submit")
stop = gr.Button("Stop")
clear = gr.Button("Clear")
with gr.Row():
with gr.Accordion("Advanced Options:", open=False):
with gr.Row():
with gr.Column():
with gr.Row():
temperature = gr.Slider(
label="Temperature",
value=0.7,
minimum=0.0,
maximum=1.0,
step=0.1,
interactive=True,
info="Higher values produce more diverse outputs",
)
with gr.Column():
with gr.Row():
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.9,
minimum=0.0,
maximum=1,
step=0.01,
interactive=True,
info=(
"Sample from the smallest possible set of tokens whose cumulative probability "
"exceeds top_p. Set to 1 to disable and sample from all tokens."
),
)
with gr.Column():
with gr.Row():
top_k = gr.Slider(
label="Top-k",
value=0,
minimum=0.0,
maximum=200,
step=1,
interactive=True,
info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
)
with gr.Column():
with gr.Row():
repetition_penalty = gr.Slider(
label="Repetition Penalty",
value=1.1,
minimum=1.0,
maximum=2.0,
step=0.1,
interactive=True,
info="Penalize repetition — 1.0 to disable.",
)
with gr.Row():
gr.Markdown(
"Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce "
"factually accurate information. The model was trained on various public datasets; while great efforts "
"have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
"biased, or otherwise offensive outputs.",
elem_classes=["disclaimer"],
)
with gr.Row():
gr.Markdown(
"[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
elem_classes=["disclaimer"],
)
submit_event = msg.submit(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
submit_click_event = submit.click(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
stop.click(
fn=None,
inputs=None,
outputs=None,
cancels=[submit_event, submit_click_event],
queue=False,
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue(max_size=128, concurrency_count=2)
demo.launch( share = True ) #delete share = True () to make it private