Bertug1911/BrtGPT-1-0719

BrtGPT-0719

Summary

This model is trained on same dataset with BrtGPT-1-Pre trained on. But model is trained on 2,1 times more data than BrtGPT-1-Pre. "0719" is for: "This check-point only" "REAL" is, on 2 August. (3,5 times more data than BrtGPT-1-Pre, 1,3 times more than BrtGPT-1-0719.)

Use

Direct use (Hugging Face Space) is cooming soon! Code use (Google Colab) (Stream):

from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
import torch
from threading import Thread

# === MODEL and TOKENIZER ===
model_id = "Bertug1911/BrtGPT-1-0719"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
model.eval().to("cuda" if torch.cuda.is_available() else "cpu")

# === CHAT ===
messages = [
    {"role": "user", "content": "How to make a cup of coffee?"},
]

# === TEMPLATE PROMPT ===
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# === STREAMER ===
streamer = TextIteratorStreamer(
    tokenizer,
    skip_prompt=True,
    skip_special_tokens=True
)

# === GENERATE ===
def generate():
    model.generate(
        input_ids=inputs,
        streamer=streamer,
        max_new_tokens=128,
        do_sample=True,
        top_k=40,
        temperature=0.8,
    )

# === THREAD START ===
thread = Thread(target=generate)
thread.start()

# === POST-Processing ===
def clean(text):
    return text.replace(" ", "").replace("Ġ", " ").replace("Ċ", "\n")

# === STREAM and CLEAN ===
for token in streamer:
    cleaned = clean(token)
    print(cleaned, end="", flush=True)

Another code (No-stream):

from transformers import pipeline

# Pipeline
pipe = pipeline(
    "text-generation",
    model="Bertug1911/BrtGPT-1-0719",
    trust_remote_code=True,
    top_k=40,              # Good for creativity
    temperature=0.8,       # Good for creativity
    max_new_tokens=128     # Default maximum model output (Maximum 1024)
)

# Messages
messages = [
    {"role": "user", "content": "What is the capital of France?"},
]

# Take out
output = pipe(messages)

# Only write asistant's (Model output) answer
assistant_response = output[0]["generated_text"][-1]["content"].strip()
# Special token conversions
formatted_out = assistant_response.replace(" ", "").replace("Ġ", " ").replace("Ċ", "\n")

print(formatted_out)

Difference beetween previus model (BrtGPT-1-Pre)

This model is slightly more good at math.

	BrtGPT-1-Pre	BrtGPT-1-0719
Basic QA	Good	Same
Code	Bad	Better, Normal
Math	Bad	Better, Normal
Creativity	Good	Same
Knowladge base QA	Normal	Same

Risks

May generates harmfull and Illegal output! USE WITH CAUTION!