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README.md

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+---
+base_model: BEE-spoke-data/smol_llama-101M-GQA-python
+datasets:
+- BEE-spoke-data/pypi_clean-deduped
+inference: false
+language:
+- en
+license: apache-2.0
+metrics:
+- accuracy
+model_creator: BEE-spoke-data
+model_name: smol_llama-101M-GQA-python
+pipeline_tag: text-generation
+quantized_by: afrideva
+source_model: BEE-spoke-data/smol_llama-101M-GQA
+tags:
+- python
+- codegen
+- markdown
+- smol_llama
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+widget:
+- example_title: Add Numbers Function
+  text: "def add_numbers(a, b):\n    return\n"
+- example_title: Car Class
+  text: "class Car:\n    def __init__(self, make, model):\n        self.make = make\n
+    \       self.model = model\n\n    def display_car(self):\n"
+- example_title: Pandas DataFrame
+  text: 'import pandas as pd
+
+    data = {''Name'': [''Tom'', ''Nick'', ''John''], ''Age'': [20, 21, 19]}
+
+    df = pd.DataFrame(data).convert_dtypes()
+
+    # eda
+
+    '
+- example_title: Factorial Function
+  text: "def factorial(n):\n    if n == 0:\n        return 1\n    else:\n"
+- example_title: Fibonacci Function
+  text: "def fibonacci(n):\n    if n <= 0:\n        raise ValueError(\"Incorrect input\")\n
+    \   elif n == 1:\n        return 0\n    elif n == 2:\n        return 1\n    else:\n"
+- example_title: Matplotlib Plot
+  text: 'import matplotlib.pyplot as plt
+
+    import numpy as np
+
+    x = np.linspace(0, 10, 100)
+
+    # simple plot
+
+    '
+- example_title: Reverse String Function
+  text: "def reverse_string(s:str) -> str:\n    return\n"
+- example_title: Palindrome Function
+  text: "def is_palindrome(word:str) -> bool:\n    return\n"
+- example_title: Bubble Sort Function
+  text: "def bubble_sort(lst: list):\n    n = len(lst)\n    for i in range(n):\n        for
+    j in range(0, n-i-1):\n"
+- example_title: Binary Search Function
+  text: "def binary_search(arr, low, high, x):\n    if high >= low:\n        mid =
+    (high + low) // 2\n        if arr[mid] == x:\n            return mid\n        elif
+    arr[mid] > x:\n"
+---
+# BEE-spoke-data/smol_llama-101M-GQA-python-GGUF
+
+Quantized GGUF model files for [smol_llama-101M-GQA-python](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 | None  |
+| [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k | None  |
+| [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m | None  |
+| [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m | None  |
+| [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m | None  |
+| [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k | None  |
+| [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 | None  |
+
+
+
+## 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](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground).
+
+- 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:
+
+```python
+# 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 :)
+
+
+
+<details>
+<summary>🔥 Unleash the Power of Code Generation! Click to Reveal the Magic! 🔮</summary>
+
+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. 
+
+```python
+"""
+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.
+
+
+</details>
+
+
+---