Instructions to use afrideva/beecoder-220M-python-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/beecoder-220M-python-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/beecoder-220M-python-GGUF",
	filename="beecoder-220m-python.fp16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use afrideva/beecoder-220M-python-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/beecoder-220M-python-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/beecoder-220M-python-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/beecoder-220M-python-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/beecoder-220M-python-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/beecoder-220M-python-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/beecoder-220M-python-GGUF:Q4_K_M

Ollama
How to use afrideva/beecoder-220M-python-GGUF with Ollama:
```
ollama run hf.co/afrideva/beecoder-220M-python-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/beecoder-220M-python-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for afrideva/beecoder-220M-python-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for afrideva/beecoder-220M-python-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/beecoder-220M-python-GGUF to start chatting

Docker Model Runner
How to use afrideva/beecoder-220M-python-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/beecoder-220M-python-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/beecoder-220M-python-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/beecoder-220M-python-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.beecoder-220M-python-GGUF-Q4_K_M

List all available models

lemonade list

afrideva commited on Jan 20, 2024

Commit

2c64be0

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1 Parent(s): f7be321

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+---
+base_model: BEE-spoke-data/beecoder-220M-python
+datasets:
+- BEE-spoke-data/pypi_clean-deduped
+- bigcode/the-stack-smol-xl
+- EleutherAI/proof-pile-2
+inference: false
+language:
+- en
+license: apache-2.0
+metrics:
+- accuracy
+model_creator: BEE-spoke-data
+model_name: beecoder-220M-python
+pipeline_tag: text-generation
+quantized_by: afrideva
+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/beecoder-220M-python-GGUF
+Quantized GGUF model files for [beecoder-220M-python](https://huggingface.co/BEE-spoke-data/beecoder-220M-python) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [beecoder-220m-python.fp16.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.fp16.gguf) | fp16 | 436.50 MB  |
+| [beecoder-220m-python.q2_k.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q2_k.gguf) | q2_k | 94.43 MB  |
+| [beecoder-220m-python.q3_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q3_k_m.gguf) | q3_k_m | 114.65 MB  |
+| [beecoder-220m-python.q4_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q4_k_m.gguf) | q4_k_m | 137.58 MB  |
+| [beecoder-220m-python.q5_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q5_k_m.gguf) | q5_k_m | 157.91 MB  |
+| [beecoder-220m-python.q6_k.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q6_k.gguf) | q6_k | 179.52 MB  |
+| [beecoder-220m-python.q8_0.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q8_0.gguf) | q8_0 | 232.28 MB  |
+## Original Model Card:
+# BEE-spoke-data/beecoder-220M-python
+This is `BEE-spoke-data/smol_llama-220M-GQA` fine-tuned for code generation on:
+- filtered version of stack-smol-XL
+- deduped version of 'algebraic stack' from proof-pile-2
+- cleaned and deduped pypi (last dataset)
+This model (and the base model) were both trained using ctx length 2048.
+## examples
+> Example script for inference testing: [here](https://gist.github.com/pszemraj/c7738f664a64b935a558974d23a7aa8c)
+It has its limitations at 220M, but seems decent for single-line or docstring generation, and/or being used for speculative decoding for such purposes.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/bLrtpr7Vi_MPvtF7mozDN.png)
+The screenshot is on CPU on a laptop.
+---