smol_llama-101M-GQA-python-GGUF/README.md

base_model, datasets, inference, language, license, metrics, model_creator, model_name, pipeline_tag, quantized_by, source_model, tags, widget

base_model	datasets	inference	language	license	metrics	model_creator	model_name	pipeline_tag	quantized_by	source_model	tags	widget
BEE-spoke-data/smol_llama-101M-GQA-python	BEE-spoke-data/pypi_clean-deduped	false	en	apache-2.0	accuracy	BEE-spoke-data	smol_llama-101M-GQA-python	text-generation	afrideva	BEE-spoke-data/smol_llama-101M-GQA	python codegen markdown smol_llama gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0	example_title text Add Numbers Function def add_numbers(a, b): return example_title text Car Class class Car: def __init__(self, make, model): self.make = make self.model = model def display_car(self): example_title text Pandas DataFrame import pandas as pd data = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]} df = pd.DataFrame(data).convert_dtypes() # eda example_title text Factorial Function def factorial(n): if n == 0: return 1 else: example_title text Fibonacci Function def fibonacci(n): if n <= 0: raise ValueError("Incorrect input") elif n == 1: return 0 elif n == 2: return 1 else: example_title text Matplotlib Plot import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 10, 100) # simple plot example_title text Reverse String Function def reverse_string(s:str) -> str: return example_title text Palindrome Function def is_palindrome(word:str) -> bool: return example_title text Bubble Sort Function def bubble_sort(lst: list): n = len(lst) for i in range(n): for j in range(0, n-i-1): example_title text Binary Search Function def binary_search(arr, low, high, x): if high >= low: mid = (high + low) // 2 if arr[mid] == x: return mid elif arr[mid] > x:

BEE-spoke-data/smol_llama-101M-GQA-python-GGUF

Quantized GGUF model files for smol_llama-101M-GQA-python from BEE-spoke-data

Name	Quant method	Size
smol_llama-101m-gqa-python.fp16.gguf	fp16	203.28 MB
smol_llama-101m-gqa-python.q2_k.gguf	q2_k	50.93 MB
smol_llama-101m-gqa-python.q3_k_m.gguf	q3_k_m	57.06 MB
smol_llama-101m-gqa-python.q4_k_m.gguf	q4_k_m	65.41 MB
smol_llama-101m-gqa-python.q5_k_m.gguf	q5_k_m	74.34 MB
smol_llama-101m-gqa-python.q6_k.gguf	q6_k	83.83 MB
smol_llama-101m-gqa-python.q8_0.gguf	q8_0	108.35 MB

Original Model Card:

smol_llama-101M-GQA: python

400MB of buzz: pure Python programming nectar! 🍯

This model is the general pre-trained checkpoint BEE-spoke-data/smol_llama-101M-GQA trained on a deduped version of pypi for +1 epoch. Play with the model in this demo space.

• Its architecture is the same as the base, with some new Python-related tokens added to vocab prior to training.
• It can generate basic Python code and markdown in README style, but will struggle with harder planning/reasoning tasks
• This is an experiment to test the abilities of smol-sized models in code generation; meaning both its capabilities and limitations

Use with care & understand that there may be some bugs 🐛 still to be worked out.

Usage

📌 Be sure to note:

1. The model uses the "slow" llama2 tokenizer. Set use_fast=False when loading the tokenizer.
2. Use transformers library version 4.33.3 due to a known issue in version 4.34.1 (at time of writing)

Which llama2 tokenizer the API widget uses is an age-old mystery, and may cause minor whitespace issues (widget only).

To install the necessary packages and load the model:

# Install necessary packages
# pip install transformers==4.33.3 accelerate sentencepiece

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(
    "BEE-spoke-data/smol_llama-101M-GQA-python",
    use_fast=False,
)
model = AutoModelForCausalLM.from_pretrained(
    "BEE-spoke-data/smol_llama-101M-GQA-python",
    device_map="auto",
)

# The model can now be used as any other decoder

longer code-gen example

Below is a quick script that can be used as a reference/starting point for writing your own, better one :)

🔥 Unleash the Power of Code Generation! Click to Reveal the Magic! 🔮

Are you ready to witness the incredible possibilities of code generation? 🚀. Brace yourself for an exceptional journey into the world of artificial intelligence and programming. Observe a script that will change the way you create and finalize code.

This script provides entry to a planet where machines can write code with remarkable precision and imagination.

"""
simple script for testing model(s) designed to generate/complete code

See details/args with the below. 
    python textgen_inference_code.py --help
"""
import logging
import random
import time
from pathlib import Path

import fire
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

logging.basicConfig(format="%(levelname)s - %(message)s", level=logging.INFO)


class Timer:
    """
    Basic timer utility.
    """

    def __enter__(self):

        self.start_time = time.perf_counter()
        return self

    def __exit__(self, exc_type, exc_value, traceback):

        self.end_time = time.perf_counter()
        self.elapsed_time = self.end_time - self.start_time
        logging.info(f"Elapsed time: {self.elapsed_time:.4f} seconds")


def load_model(model_name, use_fast=False):
    """ util for loading model and tokenizer"""
    logging.info(f"Loading model: {model_name}")
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=use_fast)
    model = AutoModelForCausalLM.from_pretrained(
        model_name, torch_dtype="auto", device_map="auto"
    )
    model = torch.compile(model)
    return tokenizer, model


def run_inference(prompt, model, tokenizer, max_new_tokens: int = 256):
    """
    run_inference

    Args:
        prompt (TYPE): Description
        model (TYPE): Description
        tokenizer (TYPE): Description
        max_new_tokens (int, optional): Description

    Returns:
        TYPE: Description
    """
    logging.info(f"Running inference with max_new_tokens={max_new_tokens} ...")
    with Timer() as timer:
        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            min_new_tokens=8,
            renormalize_logits=True,
            no_repeat_ngram_size=8,
            repetition_penalty=1.04,
            num_beams=4,
            early_stopping=True,
        )
    text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
    logging.info(f"Output text:\n\n{text}")
    return text


def main(
    model_name="BEE-spoke-data/smol_llama-101M-GQA-python",
    prompt:str=None,
    use_fast=False,
    n_tokens: int = 256,
):
    """Summary

    Args:
        model_name (str, optional): Description
        prompt (None, optional): specify the prompt directly (default: random choice from list)
        n_tokens (int, optional): max new tokens to generate
    """
    logging.info(f"Inference with:\t{model_name}, max_new_tokens:{n_tokens}")

    if prompt is None:
        prompt_list = [
            '''
            def print_primes(n: int):
               """
               Print all primes between 1 and n
               """''',
            "def quantum_analysis(",
            "def sanitize_filenames(target_dir:str, recursive:False, extension",
        ]
        prompt = random.SystemRandom().choice(prompt_list)

    logging.info(f"Using prompt:\t{prompt}")

    tokenizer, model = load_model(model_name, use_fast=use_fast)

    run_inference(prompt, model, tokenizer, n_tokens)


if __name__ == "__main__":
    fire.Fire(main)

Wowoweewa!! It can create some file cleaning utilities.

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