Text Generation
Transformers
Safetensors
GGUF
English
mixtral
tinybrainbot
mixture-of-experts
Mixture of Experts
small-language-model
from-scratch
code
python
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-360m-a75m-v5.1-python") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-360m-a75m-v5.1-python") model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-360m-a75m-v5.1-python", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M # Run inference directly in the terminal: llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M # Run inference directly in the terminal: llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
Use Docker
docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nkthebass/tinybrainbot-360m-a75m-v5.1-python" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nkthebass/tinybrainbot-360m-a75m-v5.1-python", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
- SGLang
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "nkthebass/tinybrainbot-360m-a75m-v5.1-python" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nkthebass/tinybrainbot-360m-a75m-v5.1-python", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "nkthebass/tinybrainbot-360m-a75m-v5.1-python" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nkthebass/tinybrainbot-360m-a75m-v5.1-python", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Ollama:
ollama run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Docker Model Runner:
docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
- Lemonade
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
Run and chat with the model
lemonade run user.tinybrainbot-360m-a75m-v5.1-python-Q4_K_M
List all available models
lemonade list
- Atomic Chat
|
Download code_samples.md from nkthebass/tinybrainbot-360m-a75m-v5.1-python: direct link, hf CLI and curl.
- Browser
- Download file 21.9 kB
-
https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python/resolve/main/code_samples.md
- Command line
-
hf download hf://nkthebass/tinybrainbot-360m-a75m-v5.1-python/code_samples.md
-
curl -L -o code_samples.md https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python/resolve/main/code_samples.md
21.9 kB
| # TinyBrainBot-360M-A75M-v5.1-Python: unedited samples | |
| 15 requests, each answered twice: greedy, and sampled with the recommended settings (temperature 0.4, top-p 0.9, repetition penalty 1.15). When the request asks for a function, the largest ```python block of the answer was run against a few asserts written for the test; PASS / FAIL is that result. Nothing is edited or cut, including the answers that go wrong. | |
| Function tests passed: greedy 8/11, sampled 7/11 | |
| ## FizzBuzz | |
| **greedy** - PASS | |
| *user:* | |
| Write a Python function fizzbuzz(n) that returns a list of strings for 1..n: 'Fizz' for multiples of 3, 'Buzz' for 5, 'FizzBuzz' for both, else the number as a string. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def fizzbuzz(n): | |
| result = [] | |
| for i in range(1, n+1): | |
| if i % 3 == 0 and i % 5 == 0: | |
| result.append('FizzBuzz') | |
| elif i % 3 == 0: | |
| result.append('Fizz') | |
| elif i % 5 == 0: | |
| result.append('Buzz') | |
| else: | |
| result.append(str(i)) | |
| return result | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Write a Python function fizzbuzz(n) that returns a list of strings for 1..n: 'Fizz' for multiples of 3, 'Buzz' for 5, 'FizzBuzz' for both, else the number as a string. | |
| *assistant:* | |
| Here is a Python function that implements this approach: | |
| ```python | |
| def fizzbuzz(n): | |
| result = [] | |
| for i in range(1, n + 1): | |
| if i % 3 == 0 and i % 5 == 0: | |
| result.append('FizzBuzz') | |
| elif i % 3 == 0: | |
| result.append('Fizz') | |
| elif i % 5 == 0: | |
| result.append('Buzz') | |
| else: | |
| result.append(str(i)) | |
| return result | |
| ``` | |
| ## Word count | |
| **greedy** - PASS | |
| *user:* | |
| Write a function word_count(text) that returns a dict mapping each lowercase word to how many times it appears. Ignore punctuation. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| import re | |
| def word_count(text): | |
| words = re.findall(r'\b\w+\b', text.lower()) | |
| return {word: words.count(word) for word in words} | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Write a function word_count(text) that returns a dict mapping each lowercase word to how many times it appears. Ignore punctuation. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| import re | |
| def word_count(text): | |
| words = re.findall(r'\b\w+\b', text.lower()) | |
| return {word: words.count(word) for word in words} | |
| ``` | |
| ## Palindrome | |
| **greedy** - PASS | |
| *user:* | |
| Write is_palindrome(s) that ignores case and non-letters. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def is_palindrome(s): | |
| s = ''.join(c.lower() for c in s if c.isalnum()) | |
| return s == s[::-1] | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Write is_palindrome(s) that ignores case and non-letters. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def is_palindrome(s): | |
| s = ''.join(e for e in s if e.isalnum()).lower() | |
| return s == s[::-1] | |
| ``` | |
| ## Second largest | |
| **greedy** - PASS | |
| *user:* | |
| Write a function second_largest(nums) that returns the second largest distinct number in a list, or None if there isn't one. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def second_largest(nums): | |
| if len(nums) < 2: | |
| return None | |
| unique_nums = list(set(nums)) | |
| if len(unique_nums) < 2: | |
| return None | |
| unique_nums.sort() | |
| return unique_nums[-2] | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Write a function second_largest(nums) that returns the second largest distinct number in a list, or None if there isn't one. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def second_largest(nums): | |
| if len(nums) < 2: | |
| return None | |
| unique_nums = set(nums) | |
| if len(unique_nums) < 2: | |
| return None | |
| unique_nums.remove(max(unique_nums)) | |
| return max(unique_nums) | |
| ``` | |
| ## Flatten | |
| **greedy** - PASS | |
| *user:* | |
| Write a recursive function flatten(lst) that flattens arbitrarily nested lists. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def flatten(lst): | |
| flat_list = [] | |
| for i in lst: | |
| if isinstance(i, list): | |
| flat_list.extend(flatten(i)) | |
| else: | |
| flat_list.append(i) | |
| return flat_list | |
| ``` | |
| **sampled (temp 0.4)** - FAIL: NameError: name 'flatten' is not defined | |
| *user:* | |
| Write a recursive function flatten(lst) that flattens arbitrarily nested lists. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def flatten_list(lst): | |
| flat_list = [] | |
| for item in lst: | |
| if isinstance(item, list): | |
| flat_list.extend(flatten_list(item)) | |
| else: | |
| flat_list.append(item) | |
| return flat_list | |
| ``` | |
| ## Class | |
| **greedy** - FAIL: AssertionError | |
| *user:* | |
| Write a Python class BankAccount with deposit(amount), withdraw(amount) that raises ValueError if there isn't enough money, and a balance attribute starting at 0. | |
| *assistant:* | |
| Here is a Python class BankAccount with deposit(amount), withdraw(amount) and balance attribute starting at 0. | |
| ```python | |
| class BankAccount: | |
| def __init__(self, initial_balance=0): | |
| self.balance = initial_balance | |
| def deposit(self, amount): | |
| if amount > 0: | |
| self.balance += amount | |
| return f"Deposit of {amount} successful. New balance is {self.balance}." | |
| else: | |
| return "Invalid deposit amount." | |
| def withdraw(self, amount): | |
| if amount > 0 and amount <= self.balance: | |
| self.balance -= amount | |
| return f"Withdrawal of {amount} successful. New balance is {self.balance}." | |
| else: | |
| return "Invalid withdrawal amount." | |
| def balance(self): | |
| return self.balance | |
| ``` | |
| You can use this class as follows: | |
| ```python | |
| account = BankAccount() | |
| print(account.deposit(100)) # prints: Deposit of 100 successful. New balance is 100 | |
| print(account.withdraw(30)) # prints: Withdrawal of 30 successful. New balance is 70 | |
| print(account.balance()) # prints: Balance of the account is 70 | |
| ``` | |
| **sampled (temp 0.4)** - FAIL: AttributeError: 'BankAccount' object has no attribute 'balance' | |
| *user:* | |
| Write a Python class BankAccount with deposit(amount), withdraw(amount) that raises ValueError if there isn't enough money, and a balance attribute starting at 0. | |
| *assistant:* | |
| Here is an example of how you could implement such a class: | |
| ```python | |
| class BankAccount: | |
| def __init__(self, initial_balance=0): | |
| if self.balance >= 0: | |
| raise ValueError("Balance must be positive") | |
| else: | |
| self.balance = 0 | |
| def deposit(self, amount): | |
| if amount > 0: | |
| self.balance += amount | |
| else: | |
| print("Insufficient funds") | |
| def withdraw(self, amount): | |
| if amount > 0 and amount <= self.balance: | |
| self.balance -= amount | |
| else: | |
| print("Insufficient funds") | |
| def get_balance(self): | |
| return self.balance | |
| ``` | |
| This class has three methods: `deposit()`, `withdraw()`, and `get_balance()`. The `deposit()` method adds the specified amount to the account balance, raising a `ValueError` if the balance cannot be increased. The `withdraw()` method subtracts the specified amount from the account balance, returning the new balance. The `get_balance()` method returns the current balance as the only value returned by the method. | |
| ## Nested loops | |
| **greedy** - PASS | |
| *user:* | |
| Write a function multiplication_table(n) that returns an n x n list of lists where cell [i][j] is (i+1)*(j+1). | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def multiplication_table(n): | |
| result = [] | |
| for i in range(1, n + 1): | |
| row = [] | |
| for j in range(1, n + 1): | |
| row.append(i * j) | |
| result.append(row) | |
| return result | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Write a function multiplication_table(n) that returns an n x n list of lists where cell [i][j] is (i+1)*(j+1). | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def multiplication_table(n): | |
| result = [] | |
| for i in range(1, n + 1): | |
| row = [] | |
| for j in range(1, n + 1): | |
| row.append(i * j) | |
| result.append(row) | |
| return result | |
| ``` | |
| ## Dict sort | |
| **greedy** - FAIL: AssertionError | |
| *user:* | |
| Given a dict of name -> score, write top_n(scores, n) returning the n names with the highest scores, highest first. | |
| *assistant:* | |
| Here is a Python solution using collections.Counter: | |
| ```python | |
| def top_n(scores, n): | |
| # Count the occurrences of each name in the list | |
| name_counts = {} | |
| for name in scores: | |
| if name in name_counts: | |
| name_counts[name] += 1 | |
| else: | |
| name_counts[name] = 1 | |
| # Get the n names with the highest count | |
| top_n_names = [name for name, count in name_counts.items() if count == max(name_counts.values())] | |
| return top_n_names | |
| # Test the function | |
| scores = {'John': 85, 'Jane': 80, 'Bob': 70, 'Mary': 70} | |
| n = 3 | |
| print(top_n(scores, n)) | |
| ``` | |
| This script first counts the occurrences of each name in the list. Then it finds the names with the highest count. The script then returns the names that have the highest count. | |
| Please note that this solution assumes that the names are unique. If there are duplicate names, the script will not return the names that have the highest count. | |
| **sampled (temp 0.4)** - FAIL: TypeError: '>' not supported between instances of 'int' and 'list' | |
| *user:* | |
| Given a dict of name -> score, write top_n(scores, n) returning the n names with the highest scores, highest first. | |
| *assistant:* | |
| Here is a Python solution using collections.defaultdict: | |
| ```python | |
| def top_n(scores, n): | |
| # Create a defaultdict to store the most common first elements | |
| top_n = {} | |
| for key, value in scores.items(): | |
| if value > 0: | |
| top_n[key] = max(top_n.get(key, []), value) | |
| return sorted(top_n.keys(), key=lambda k: (-top_n[k], k)) | |
| ``` | |
| In this code, we initialize an empty dictionary `top_n` to store the keys and values of the dictionaries. We then iterate over each key-value pair in the input list `scores`. For each key-value pair, we check if the value is greater than zero. If it is, we update the corresponding key in `top_n` with the maximum of the current value and the current key's value. This way, we keep track of the top-n most common first elements for each unique key. Finally, we return the sorted list of keys by their values. | |
| Please note that this solution assumes that all dictionaries have the same size as the input list. If the input list can be large or small, you may need to adjust the solution accordingly. | |
| ## CSV | |
| **greedy** - FAIL: TypeError: unsupported operand type(s) for +: 'int' and 'list' | |
| *user:* | |
| Write a function average_column(path, column) that reads a CSV file with a header row and returns the average of the numeric column. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| import csv | |
| def average_column(path, column): | |
| with open(path, 'r') as file: | |
| reader = csv.reader(file) | |
| next(reader) # Skip the header row | |
| return sum(reader) / len(reader) | |
| ``` | |
| **sampled (temp 0.4)** - FAIL: TypeError: string indices must be integers, not 'str' | |
| *user:* | |
| Write a function average_column(path, column) that reads a CSV file with a header row and returns the average of the numeric column. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def average_column(path, column): | |
| return sum(row[column] for row in open(path)) / len(open(path)) | |
| ``` | |
| ## Debug | |
| **greedy** - PASS | |
| *user:* | |
| This function should return the sum of the even numbers, but it's wrong. Fix it: | |
| ```python | |
| def sum_even(nums): | |
| total = 0 | |
| for n in nums: | |
| if n % 2 == 1: | |
| total += n | |
| return total | |
| ``` | |
| *assistant:* | |
| The issue with the previous function is that it's not checking if the number is even or not. It's checking if the number is divisible by 2 without a remainder. | |
| Here's the corrected function: | |
| ```python | |
| def sum_even(nums): | |
| total = 0 | |
| for n in nums: | |
| if n % 2 == 0: | |
| total += n | |
| return total | |
| ``` | |
| This function works by iterating through each number in the input list and checking if it's even. If it is, it adds it to the total. Finally, it returns the total sum. | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| This function should return the sum of the even numbers, but it's wrong. Fix it: | |
| ```python | |
| def sum_even(nums): | |
| total = 0 | |
| for n in nums: | |
| if n % 2 == 1: | |
| total += n | |
| return total | |
| ``` | |
| *assistant:* | |
| Here is a corrected version of your function: | |
| ```python | |
| def sum_even(nums): | |
| total = 0 | |
| for n in nums: | |
| if n % 2 == 0: | |
| total += n | |
| return total | |
| ``` | |
| In this version, I added an additional condition to check if the number is divisible by 2. If it is, I added 1 to the total sum. This way, we are only adding even numbers and not checking for divisibility. | |
| ## Explain | |
| **greedy** | |
| *user:* | |
| What does this code do? | |
| ```python | |
| result = {k: v for k, v in zip(keys, values) if v is not None} | |
| ``` | |
| *assistant:* | |
| Here's how you can implement this: | |
| ```python | |
| def zip_keys_values(keys, values): | |
| return list(zip(keys, values)) | |
| ``` | |
| **sampled (temp 0.4)** | |
| *user:* | |
| What does this code do? | |
| ```python | |
| result = {k: v for k, v in zip(keys, values) if v is not None} | |
| ``` | |
| *assistant:* | |
| Here's how you can implement the `zip_with_none` function: | |
| ```python | |
| def zip_with_none(keys, values): | |
| return dict((k, v) for k, v in zip(keys, values)) | |
| ``` | |
| ## Write tests | |
| **greedy** | |
| *user:* | |
| Write three pytest test functions for a function add(a, b) that adds two numbers. | |
| *assistant:* | |
| Here is a Python function that implements this approach: | |
| ```python | |
| def add(a, b): | |
| return a + b | |
| def test_add(): | |
| assert add(2, 3) == 5 | |
| assert add(5, 4) == 9 | |
| assert add(10, 2) == 20 | |
| assert add(-1, -2) == -5 | |
| assert add(2, -3) == -7 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -8) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -8) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| assert add(10, -7) == -13 | |
| assert add(2, -9) == -15 | |
| assert add(5, -6) == -12 | |
| **sampled (temp 0.4)** | |
| *user:* | |
| Write three pytest test functions for a function add(a, b) that adds two numbers. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def add(a, b): | |
| return a + b | |
| # Test cases | |
| assert add(1, 2) == 3 | |
| assert add(2, 3) == 5 | |
| assert add(4, 5) == 9 | |
| ``` | |
| ## Multi-turn | |
| **greedy** | |
| *user:* | |
| Write a function that returns the factorial of n. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def factorial(n): | |
| if n == 0: | |
| return 1 | |
| else: | |
| return n * factorial(n-1) | |
| ``` | |
| *user:* | |
| Now add type hints and a docstring. | |
| *assistant:* | |
| Here's a revised version of the function: | |
| ```python | |
| def factorial(n: int) -> int: | |
| """ | |
| Returns the factorial of n. | |
| Args: | |
| n (int): The input integer. | |
| Returns: | |
| int: The factorial of n. | |
| Raises: | |
| TypeError: If n is not an integer. | |
| """ | |
| if not isinstance(n, int): | |
| raise TypeError("Input must be an integer.") | |
| if n < 0: | |
| raise ValueError("Input must be a non-negative integer.") | |
| if n == 0: | |
| return 1 | |
| return n * factorial(n-1) | |
| ``` | |
| **sampled (temp 0.4)** | |
| *user:* | |
| Write a function that returns the factorial of n. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def fact(n): | |
| if n == 0 or n == 1: | |
| return 1 | |
| else: | |
| return n * fact(n-1) | |
| ``` | |
| *user:* | |
| Now add type hints and a docstring. | |
| *assistant:* | |
| Here's how you can do it in Python: | |
| ```python | |
| def fact(n): | |
| """ | |
| Returns the factorial of n. | |
| Args: | |
| n (int): The number for which to calculate the factorial. | |
| Returns: | |
| int: The factorial of n. | |
| Example: | |
| fact(5) = 5! | |
| fact(3) = 3! | |
| fact(4) = 4! | |
| fact(2) = 2 | |
| fact(1) = 1 | |
| fact(0) = 1 | |
| fact(6) = 6! | |
| fact(4) = 24 | |
| fact(8) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(8) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) = 6 * 6 * 6 | |
| fact(4) = 24 * 6 * 8 | |
| fact(2) = 2 * 6 * 2 | |
| fact(1) = 1 | |
| fact(6) | |
| ## Binary search | |
| **greedy** - PASS | |
| *user:* | |
| Implement binary_search(arr, target) on a sorted list; return the index or -1. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def binary_search(arr, target): | |
| low = 0 | |
| high = len(arr) - 1 | |
| while low <= high: | |
| mid = (low + high) // 2 | |
| if arr[mid] == target: | |
| return mid | |
| elif arr[mid] < target: | |
| low = mid + 1 | |
| else: | |
| high = mid - 1 | |
| return -1 | |
| ``` | |
| **sampled (temp 0.4)** - PASS | |
| *user:* | |
| Implement binary_search(arr, target) on a sorted list; return the index or -1. | |
| *assistant:* | |
| Here is the code to solve this problem: | |
| ```python | |
| def binary_search(arr, target): | |
| low = 0 | |
| high = len(arr) - 1 | |
| while low <= high: | |
| mid = (low + high) // 2 | |
| if arr[mid] == target: | |
| return mid | |
| elif arr[mid] < target: | |
| low = mid + 1 | |
| else: | |
| high = mid - 1 | |
| return -1 | |
| ``` | |
| ## Plain chat | |
| **greedy** | |
| *user:* | |
| hey, what's the difference between a list and a tuple in python? | |
| *assistant:* | |
| In Python, a list and a tuple are both used to store data, but they have some key differences. | |
| A list is an unordered collection of items. It is a collection of elements that can be added, removed, or modified. It is an unordered collection of items. | |
| A tuple, on the other hand, is an ordered collection of items. It is an unordered collection of items. It is an ordered collection of items. | |
| Here's an example of a list and a tuple: | |
| ```python | |
| list = [1, 2, 3, 4, 5] | |
| tuple = (4, 5) | |
| ``` | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an unordered collection of items, and the tuple is a list of 4 items. | |
| In the second example, the list is [1, 2, 3, 4, 5]. The list is an ordered collection of items, and the tuple is a list of 5 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an unordered collection of items, and the tuple is a list of 4 items. | |
| In the second example, the list is [1, 2, 3, 4, 5]. The list is an ordered collection of items, and the tuple is a list of 5 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an unordered collection of items, and the tuple is a list of 4 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an ordered collection of items, and the tuple is a list of 5 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an unordered collection of items, and the tuple is a list of 4 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an ordered collection of items, and the tuple is a list of 5 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an unordered collection of items, and the tuple is a list of 4 items. | |
| In the first example, the list is [1, 2, 3, 4, 5]. The list is an ordered collection of items, and | |
| **sampled (temp 0.4)** | |
| *user:* | |
| hey, what's the difference between a list and a tuple in python? | |
| *assistant:* | |
| A list is an unordered collection of items, while a tuple is an ordered collection of elements. For example: | |
| ```python | |
| list_elements = [1, 2, 3, 4, 5] | |
| tuple_elements = (5,) | |
| ``` | |
| In Python, tuples are immutable, meaning they cannot be modified after creation. So, if you want to add a new item to the list, you can use `tuple()` instead: | |
| ```python | |
| list_elements += [1, 2, 3, 4, 5] | |
| ``` | |
| The `+=` operator is used to add a new element to the end of the list. | |