Starchat Alpha

Table of Contents

Model Overview

The StarChat Alpha model is a language model designed to act as a helpful coding assistant. It’s the first in a series of models fine-tuned from the bigcode/starcoderbase model.

Key Attributes

• Model Type: A 16B parameter GPT-like model
• Language: English
• License: BigCode Open RAIL-M v1
• Finetuned from: bigcode/starcoderbase model

What can it do?

• Help with programming tasks
• Generate code snippets
• Assist with coding-related questions

Tasks and rank

Text-to-Code Generation 65th

Code Generation 79th

Code Documentation Generation 27th

Code Completion 79th

Learn More About the Ranking System

Capabilities

The StarChat Alpha model is a powerful coding assistant that can help with programming tasks. It’s been fine-tuned on a blend of the oasst1 and databricks-dolly-15k datasets. This means it’s really good at understanding and generating code in English.

What can it do?

• Generate code: It can create code snippets in response to prompts or questions. For example, you can ask it to sort a list in Python, and it will provide a code example.
• Answer programming questions: The model can answer questions about programming concepts, such as how to use a specific library or function.
• Assist with coding tasks: It can help with tasks like debugging, code completion, and code review.

Strengths

• Large knowledge base: The model has been trained on a massive dataset of code, which means it has a broad knowledge base to draw from.
• Fine-tuned for coding: It has been specifically fine-tuned for coding tasks, which makes it more effective than general-purpose language models.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

DeepSeek Coder V2 Instruct GGUF 27 Deep learning model

OpenCodeInterpreter CL 70B 24 Code generation model

Performance

StarChat Alpha is a powerful language model that showcases remarkable performance in various tasks. But how does it really perform?

Speed

Let’s talk about speed. It’s a 16B parameter model, which is relatively large. But don’t worry, it’s designed to handle big tasks efficiently. With the right hardware, you can expect fast response times, even with complex queries.

Accuracy

Accuracy is crucial in language models. StarChat Alpha has been fine-tuned on a blend of the oasst1 and databricks-dolly-15k datasets, which helps it understand the nuances of programming languages. But, it’s not perfect. The model may produce problematic outputs, especially when prompted to do so.

Efficiency

Efficiency is key when it comes to language models. StarChat Alpha is designed to be efficient in its processing, but it’s not without its limitations. The model may produce code snippets that are syntactically valid but semantically incorrect. It’s essential to carefully inspect the outputs before using them.

Limitations

While StarChat Alpha is a powerful tool, it’s essential to remember its limitations. The model may produce problematic outputs, and it’s not perfect. Always carefully inspect the outputs before using them.

Lack of Alignment with Human Preferences

Unlike some other AI models, StarChat Alpha has not been aligned with human preferences using techniques like RLHF (Reinforcement Learning from Human Feedback). This means that the model may generate content that is not suitable for all audiences, especially when prompted to do so.

Biased Demographics

The model was trained primarily on code data from GitHub, which means that it may reflect the demographics of the GitHub community. This can lead to biased outputs, particularly when it comes to issues like gender representation.

Code Quality Issues

StarChat Alpha can produce code snippets that are syntactically valid but semantically incorrect. This means that the code may not compile or may produce incorrect results. Additionally, the model may generate code that is vulnerable to security exploits.

Examples

How do I sort a list in Python? You can sort a list in Python by using the sort() method. Here's an example: numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5] numbers.sort() print(numbers)

Write a Python code snippet to calculate the area of a rectangle. def calculate_area(length, width): area = length * width return area rectangle_length = 5 rectangle_width = 3 print(calculate_area(rectangle_length, rectangle_width))

Explain how to use the break statement in Python. The break statement in Python is used to exit a loop prematurely. Here's an example: for i in range(1, 11): if i == 5: break print(i)

Getting Started

To get started with the StarChat Alpha model, you can use the following code:

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-alpha", torch_dtype=torch.bfloat16, device_map="auto")

prompt_template = "<|system|>\n<|end|>\n<|user|>\n{query}<|end|>\n<|assistant|>"
prompt = prompt_template.format(query="How do I sort a list in Python?")

outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.2, top_k=50, top_p=0.95, eos_token_id=49155)

This code will generate a response to the prompt “How do I sort a list in Python?” using the StarChat Alpha model.

Format

Architecture

StarChat Alpha is a 16B parameter GPT-like model, which is a type of transformer architecture. This means it’s made up of many layers that process input in parallel, allowing it to handle complex patterns in data.

Supported Data Formats

StarChat Alpha works with English language text data, specifically designed to handle code-related tasks. It’s been fine-tuned on a blend of the oasst1 and databricks-dolly-15k datasets.

Input Requirements

When using StarChat Alpha, you’ll need to format your input text in a specific way. Here’s an example:

prompt_template = "<|system|>\n<|end|>\n<|user|>\n{query}<|end|>\n<|assistant|>"
prompt = prompt_template.format(query="How do I sort a list in Python?")

Notice the special <|end|> token with ID 49155 that denotes the end of a turn.

Output Handling

When you run the model, it will generate text output. You can control the length of the output by setting the max_new_tokens parameter. For example:

outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.2, top_k=50, top_p=0.95, eos_token_id=49155)

This will generate output with a maximum length of 256 tokens.