Qwen25_Coder_MultipleChoice
This project focuses on distilling YAML-based structured multi-step reasoning capabilities from the GPT-4o teacher model into the smaller Qwen2.5 Coder 1.5B-Instruct LLM.
This document provides guidance on getting started with
tuandunghcmut/Qwen25_Coder_MultipleChoice, a model fine-tuned for multiple-choice coding questions.A demonstration notebook is available on Google Colab (click the badge below). Please note that the training code has been omitted from this notebook. It is intended solely for testing and inference using the latest checkpoint.
Note: The initial training was conducted on a dataset with errors rather than a perfectly preprocessed one—garbage in, garbage out. As a result, while the model successfully adheres to the desired YAML format and demonstrates structured reasoning, its performance remains unstable. Future iterations will focus on retraining with a more extensive, high-quality dataset to improve stability and accuracy.
Apologies for the current state of the project. The initial version has some inconsistencies due to training on the old dataset, tuandunghcmut/normal_dataset. Future plans include refactoring the code into a more structured format, expanding the dataset to the new one, tuandunghcmut/coding-mcq-reasoning, and retraining the model using distributed training for improved scalability. Additionally, I plan to train on a larger, high-quality dataset to enhance performance and ensure better stability.
The guide below provides an explanation of the code presented in the notebook. I hope you will understand my ideas and the structure of the code.
Installation
First, install the required dependencies:
# Install core dependencies
pip install transformers torch pandas
# For faster inference (important)
pip install unsloth accelerate bitsandbytes
# Flash Attention (highly recommended for speed)
pip install flash-attn --no-build-isolation
# For dataset handling and YAML parsing
pip install datasets pyyaml
Key Classes
The project provides several key classes for working with the model:
1. QwenModelHandler
class QwenModelHandler:
"""Handler for Qwen models with inference and saving capabilities using Unsloth"""
def __init__(self, model_name="unsloth/Qwen2.5-7B", max_seq_length=768,
quantization=None, device_map="auto", cache_dir=None):
"""
Initialize model and tokenizer using Unsloth
Args:
model_name: Name or path of the model (preferably an unsloth model)
max_seq_length: Maximum sequence length for the model
quantization: Quantization type (None, '4bit', '8bit') - for compatibility
device_map: Device mapping strategy
cache_dir: Cache directory for models
"""
This class handles the core model operations:
- Model loading and initialization
- Text generation with streaming support
- Perplexity calculation
- Model saving and pushing to HuggingFace Hub
2. PromptCreator
class PromptCreator:
"""Creates and formats prompts for multiple choice questions"""
# Prompt types
BASIC = "basic" # Simple answer-only format
YAML_REASONING = "yaml" # YAML formatted reasoning
TEACHER_REASONED = "teacher" # Same YAML format but using teacher completions
This class manages prompt creation with three modes:
- Basic: Simple answer-only format
- YAML Reasoning: Structured reasoning in YAML format
- Teacher Reasoned: YAML format with teacher completions for training
3. ResponseParser
class ResponseParser:
"""Parser for model responses with support for different formats"""
# Parser modes
BASIC = "basic" # Extract single letter answer
YAML = "yaml" # Parse YAML formatted response with reasoning
This class handles response parsing:
- Extracts answers from model responses
- Parses YAML-formatted reasoning
- Supports both basic and YAML formats
4. MultipleChoiceTester
class MultipleChoiceTester:
"""Framework for testing Qwen models on multiple choice questions"""
def __init__(self, model_handler, prompt_creator=None):
"""
Initialize with model handler and prompt configuration
Args:
model_handler: The QwenModelHandler instance
prompt_creator: Optional PromptCreator instance
"""
This class provides a complete testing framework:
- Single example inference
- Batch processing
- Dataset evaluation
- Performance metrics tracking
- Results saving and visualization
Full Class Implementations
Click to expand/collapse full class implementations
1. QwenModelHandler
class QwenModelHandler:
"""Handler for Qwen models with inference and saving capabilities using Unsloth"""
def __init__(self, model_name="unsloth/Qwen2.5-7B", max_seq_length=768,
quantization=None, device_map="auto", cache_dir=None):
self.model_name = model_name
self.max_seq_length = max_seq_length
self.device_map = device_map
self.quantization = quantization
self.cache_dir = cache_dir
# Convert quantization parameter to load_in_4bit parameter for Unsloth
self.load_in_4bit = quantization == "4bit"
# Load tokenizer and model
self.tokenizer, self.model = self._load_model()
self.response_parser = ResponseParser()
def _load_model(self):
"""Load model and tokenizer with Unsloth for optimization"""
from unsloth import FastLanguageModel
import torch
print(f"Loading {self.model_name} with Unsloth, max_seq_length={self.max_seq_length}")
# Set dtype based on hardware
dtype = None # None for auto detection
# Load model and tokenizer with Unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=self.model_name,
max_seq_length=self.max_seq_length,
dtype=dtype,
load_in_4bit=self.load_in_4bit,
cache_dir=self.cache_dir,
)
return tokenizer, model
def generate_with_streaming(self, prompt, temperature=0.7, max_tokens=1024, stream=True):
"""Generate completion with optional streaming using Unsloth's optimized inference"""
# Enable faster inference
from unsloth import FastLanguageModel
FastLanguageModel.for_inference(self.model)
# Format as chat
messages = [{"role": "user", "content": prompt}]
chat_text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize input
model_inputs = self.tokenizer([chat_text], return_tensors="pt").to(self.model.device)
# Generate with streaming if requested
if stream:
from transformers import TextIteratorStreamer
import threading
# Set up streamer
streamer = TextIteratorStreamer(
self.tokenizer,
skip_prompt=True,
skip_special_tokens=True
)
# Start generation in a thread
generation_kwargs = {
"input_ids": model_inputs.input_ids,
"attention_mask": model_inputs.attention_mask,
"temperature": temperature,
"max_new_tokens": max_tokens,
"streamer": streamer,
"do_sample": temperature > 0.0,
"use_cache": True,
"min_p": 0.1 if temperature > 0.0 else None,
}
thread = threading.Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
return streamer
else:
# Generate without streaming
generated_ids = self.model.generate(
input_ids=model_inputs.input_ids,
attention_mask=model_inputs.attention_mask,
temperature=temperature,
max_new_tokens=max_tokens,
do_sample=temperature > 0.0,
use_cache=True,
min_p=0.1 if temperature > 0.0 else None,
)
# Decode the generated text
generated_text = self.tokenizer.decode(
generated_ids[0][model_inputs.input_ids.shape[1]:],
skip_special_tokens=True
)
return generated_text
def calculate_perplexity(self, prompt, answer, temperature=0.0):
"""Calculate perplexity for a prompt and answer pair"""
import torch
# Format chat for perplexity calculation
messages = [
{"role": "user", "content": prompt},
{"role": "assistant", "content": answer}
]
chat_text = self.tokenizer.apply_chat_template(
messages,
tokenize=False
)
# Tokenize the text
encodings = self.tokenizer(chat_text, return_tensors="pt").to(self.model.device)
# Calculate loss
with torch.no_grad():
outputs = self.model(**encodings, labels=encodings.input_ids)
# Get loss and calculate perplexity
neg_log_likelihood = outputs.loss.item()
perplexity = torch.exp(torch.tensor(neg_log_likelihood)).item()
return perplexity
def save_model(self, output_dir, save_method="lora"):
"""Save model to disk using Unsloth's optimized methods"""
import os
os.makedirs(output_dir, exist_ok=True)
# Use Unsloth's saving methods
if save_method == "lora":
self.model.save_pretrained(output_dir)
self.tokenizer.save_pretrained(output_dir)
elif save_method == "merged_16bit":
self.model.save_pretrained_merged(output_dir, self.tokenizer, save_method="merged_16bit")
elif save_method == "merged_4bit":
self.model.save_pretrained_merged(output_dir, self.tokenizer, save_method="merged_4bit")
elif save_method == "gguf":
self.model.save_pretrained_gguf(output_dir, self.tokenizer, quantization_method="q4_k_m")
else:
raise ValueError(f"Unknown save method: {save_method}")
print(f"Model saved to {output_dir} using method {save_method}")
return output_dir
def push_to_hub(self, repo_id, token=None, save_method="lora", private=False):
"""Push model to Hugging Face Hub using Unsloth's optimized methods"""
if save_method == "lora":
self.model.push_to_hub_merged(repo_id, self.tokenizer, save_method="lora", token=token)
elif save_method == "merged_16bit":
self.model.push_to_hub_merged(repo_id, self.tokenizer, save_method="merged_16bit", token=token)
elif save_method == "merged_4bit":
self.model.push_to_hub_merged(repo_id, self.tokenizer, save_method="merged_4bit", token=token)
elif save_method == "gguf":
self.model.push_to_hub_gguf(
repo_id,
self.tokenizer,
quantization_method=["q4_k_m", "q5_k_m"],
token=token
)
else:
raise ValueError(f"Unknown save method: {save_method}")
print(f"Model successfully pushed to: https://huggingface.co/{repo_id}")
return f"https://huggingface.co/{repo_id}"
2. PromptCreator
class PromptCreator:
"""Creates and formats prompts for multiple choice questions"""
# Prompt types
BASIC = "basic" # Simple answer-only format
YAML_REASONING = "yaml" # YAML formatted reasoning
TEACHER_REASONED = "teacher" # Same YAML format but using teacher completions
def __init__(self, prompt_type=BASIC):
if prompt_type == self.TEACHER_REASONED:
prompt_type = self.YAML_REASONING
self.prompt_type = prompt_type
self.original_type = prompt_type
def format_choices(self, choices):
"""Format choices as a lettered list"""
return "\n".join(
[f"{chr(65 + i)}. {choice}" for i, choice in enumerate(choices)]
)
def get_max_letter(self, choices):
"""Get the maximum letter based on number of choices"""
return chr(65 + len(choices) - 1)
def create_inference_prompt(self, question, choices):
"""Create a prompt for inference based on current prompt type"""
formatted_choices = self.format_choices(choices)
max_letter = self.get_max_letter(choices)
if self.prompt_type == self.YAML_REASONING:
return self._create_yaml_prompt(question, formatted_choices, max_letter)
else:
return self._create_basic_prompt(question, formatted_choices, max_letter)
def _create_basic_prompt(self, question, formatted_choices, max_letter):
"""Create a basic prompt asking for just the answer letter"""
return f"""
QUESTION:
{question}
CHOICES:
{formatted_choices}
Answer with a single letter from A through {max_letter} without any additional explanation or commentary.
"""
def _create_yaml_prompt(self, question, formatted_choices, max_letter):
"""Create a prompt requesting YAML-formatted reasoning"""
return f"""
QUESTION:
{question}
CHOICES:
{formatted_choices}
Analyze this question step-by-step and provide a detailed explanation.
Your response MUST be in YAML format as follows:
understanding: |
<your understanding of what the question is asking>
analysis: |
<your analysis of each option>
reasoning: |
<your step-by-step reasoning process>
conclusion: |
<your final conclusion>
answer: <single letter A through {max_letter}>
The answer field MUST contain ONLY a single character letter.
"""
def create_training_prompt(self, question, choices):
"""Create a prompt for training with the current prompt type"""
formatted_choices = self.format_choices(choices)
max_letter = self.get_max_letter(choices)
if self.prompt_type == self.YAML_REASONING:
return self._create_yaml_training_prompt(
question, formatted_choices, max_letter
)
else:
return self._create_basic_training_prompt(
question, formatted_choices, max_letter
)
def _create_basic_training_prompt(self, question, formatted_choices, max_letter):
"""Create a basic training prompt"""
return f"""
QUESTION:
{question}
CHOICES:
{formatted_choices}
The answer is a single letter (A, B, C, etc.). Only provide ONE character as your answer:
"""
def _create_yaml_training_prompt(self, question, formatted_choices, max_letter):
"""Create a YAML-formatted training prompt"""
return f"""
QUESTION:
{question}
CHOICES:
{formatted_choices}
Analyze this question step-by-step and provide a detailed explanation.
Follow the YAML format in your response:
understanding: |
<your understanding of the question>
analysis: |
<your analysis of each option>
reasoning: |
<your reasoning about the correct answer>
conclusion: |
<your final conclusion>
answer: <single letter A through {max_letter}>
"""
def set_prompt_type(self, prompt_type):
"""Set the prompt type"""
self.original_type = prompt_type
if prompt_type == self.TEACHER_REASONED:
pass
self.prompt_type = prompt_type
return self
def is_teacher_mode(self):
"""Check if we're using teacher mode"""
return self.original_type == self.TEACHER_REASONED
3. ResponseParser
class ResponseParser:
"""Parser for model responses with support for different formats"""
# Parser modes
BASIC = "basic" # Extract single letter answer
YAML = "yaml" # Parse YAML formatted response with reasoning
def __init__(self, parser_mode=BASIC):
self.parser_mode = parser_mode
def parse(self, response_text):
"""Parse the model's response according to the current mode"""
if self.parser_mode == self.YAML:
return self._parse_yaml_response(response_text)
else:
return self._parse_basic_response(response_text)
def _parse_basic_response(self, response_text):
"""Parse basic response looking for a letter answer"""
import re
# Try to extract a single letter answer (A-Z)
answer_match = re.search(r"(?:^|\s)([A-Z])(?:\s|$|\.)", response_text)
if answer_match:
answer = answer_match.group(1)
else:
# Take first character if it's a letter
if response_text and response_text[0].isalpha():
answer = response_text[0].upper()
else:
answer = None
# For basic mode, we don't extract detailed reasoning
reasoning = ""
return answer, reasoning
def _parse_yaml_response(self, response_text):
"""Parse YAML formatted response extracting answer and reasoning"""
import re
import yaml
# First try to find answer in YAML format
yaml_match = re.search(r"answer:\s*([A-Z])", response_text)
if yaml_match:
answer = yaml_match.group(1)
else:
# Fall back to basic extraction if YAML parsing fails
answer_match = re.search(r"(?:^|\s)([A-Z])(?:\s|$|\.)", response_text)
if answer_match:
answer = answer_match.group(1)
elif response_text and response_text[0].isalpha():
answer = response_text[0].upper()
else:
answer = None
# Try to parse reasoning from YAML format
reasoning = ""
if "reasoning:" in response_text:
yaml_content = yaml.safe_load("---\n" + response_text)
if isinstance(yaml_content, dict) and "reasoning" in yaml_content:
reasoning = yaml_content["reasoning"]
# Add other YAML fields if available
if "understanding" in yaml_content:
reasoning = f"Understanding: {yaml_content['understanding']}\n\n{reasoning}"
if "conclusion" in yaml_content:
reasoning = f"{reasoning}\n\nConclusion: {yaml_content['conclusion']}"
else:
# Use the full response as reasoning if not in YAML format
reasoning = response_text
return answer, reasoning
def set_parser_mode(self, parser_mode):
"""Set the parser mode"""
self.parser_mode = parser_mode
return self
@classmethod
def from_prompt_type(cls, prompt_type):
"""Create a parser instance with mode matching the prompt type"""
if prompt_type == PromptCreator.YAML_REASONING or prompt_type == PromptCreator.TEACHER_REASONED:
return cls(parser_mode=cls.YAML)
else:
return cls(parser_mode=cls.BASIC)
4. MultipleChoiceTester
class MultipleChoiceTester:
"""Framework for testing Qwen models on multiple choice questions"""
def __init__(self, model_handler, prompt_creator=None):
self.model_handler = model_handler
self.prompt_creator = prompt_creator or PromptCreator(PromptCreator.BASIC)
self.response_parser = ResponseParser.from_prompt_type(self.prompt_creator.prompt_type)
def infer_example(self, example, temperature=0.7, max_tokens=1024, prompt_type=None, stream=False):
"""Inference on a single example for visualization/demonstration"""
# Allow temporary override of prompt type
original_prompt_type = None
if prompt_type is not None:
original_prompt_type = self.prompt_creator.prompt_type
self.prompt_creator.set_prompt_type(prompt_type)
self.response_parser = ResponseParser.from_prompt_type(prompt_type)
# Prepare data
question = example["question"]
# Handle different formats of choices
if isinstance(example["choices"], list):
choices = example["choices"]
elif isinstance(example["choices"], str) and example["choices"].startswith("["):
import ast
choices = ast.literal_eval(example["choices"]) if "[" in example["choices"] else example["choices"].split(",")
else:
choices = str(example["choices"]).split(",")
# Generate the prompt using prompt creator
prompt = self.prompt_creator.create_inference_prompt(question, choices)
# Start timing
start_time = time.time()
if stream:
# Use streaming generation
streamer = self.model_handler.generate_with_streaming(
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
stream=True
)
# Collect output from streamer
raw_response = ""
print("Model response:")
for text_chunk in streamer:
print(text_chunk, end="", flush=True)
raw_response += text_chunk
print("\n")
else:
# Generate without streaming
raw_response = self.model_handler.generate_with_streaming(
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
stream=False
)
response_time = time.time() - start_time
# Parse the response using the response parser
predicted_answer, reasoning = self.response_parser.parse(raw_response)
# Prepare results
result = {
"question": question,
"choices": choices,
"predicted_answer": predicted_answer,
"reasoning": reasoning,
"response_time": response_time,
"raw_response": raw_response,
"prompt_type": self.prompt_creator.prompt_type,
}
# Add task_id if available
if "task_id" in example:
result["task_id"] = example["task_id"]
# Calculate metrics if label is provided
if "answer" in example:
label = example["answer"]
result["correct_answer"] = label
result["is_correct"] = predicted_answer == label
# Calculate perplexity if requested
if hasattr(self.model_handler, "calculate_perplexity"):
perplexity = self.model_handler.calculate_perplexity(prompt, raw_response)
result["perplexity"] = perplexity
# Restore original prompt type if it was overridden
if original_prompt_type is not None:
self.prompt_creator.set_prompt_type(original_prompt_type)
self.response_parser = ResponseParser.from_prompt_type(original_prompt_type)
return result
def infer_batch(self, examples, temperature=0.7, max_tokens=1024, prompt_type=None, batch_size=4):
"""Inference on a batch of examples"""
# Allow temporary override of prompt type
original_prompt_type = None
if prompt_type is not None:
original_prompt_type = self.prompt_creator.prompt_type
self.prompt_creator.set_prompt_type(prompt_type)
self.response_parser = ResponseParser.from_prompt_type(prompt_type)
# Prepare all prompts
prompts = []
metadata = []
for i, example in enumerate(examples):
# Extract data
question = example["question"]
# Handle different formats of choices
if isinstance(example["choices"], list):
choices = example["choices"]
elif isinstance(example["choices"], str) and example["choices"].startswith("["):
import ast
choices = ast.literal_eval(example["choices"]) if "[" in example["choices"] else example["choices"].split(",")
else:
choices = str(example["choices"]).split(",")
# Generate the prompt using prompt creator
prompt = self.prompt_creator.create_inference_prompt(question, choices)
prompts.append(prompt)
# Store metadata for later
meta = {
"question": question,
"choices": choices,
"index": i,
}
# Add label if available
if "answer" in example:
meta["label"] = example["answer"]
if "task_id" in example:
meta["task_id"] = example["task_id"]
metadata.append(meta)
# Process in batches
results = []
correct_count = 0
total_count = 0
perplexities = []
for i in range(0, len(prompts), batch_size):
batch_prompts = prompts[i:i+batch_size]
batch_meta = metadata[i:i+batch_size]
# Process batch
start_time = time.time()
batch_responses = []
for prompt in batch_prompts:
response = self.model_handler.generate_with_streaming(
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
stream=False
)
batch_responses.append(response)
batch_time = time.time() - start_time
# Process each response in the batch
for j, (response, meta) in enumerate(zip(batch_responses, batch_meta)):
# Parse response
predicted_answer, reasoning = self.response_parser.parse(response)
# Create result
result = {
"question": meta["question"],
"choices": meta["choices"],
"predicted_answer": predicted_answer,
"reasoning": reasoning,
"raw_response": response,
"prompt_type": self.prompt_creator.prompt_type,
"response_time": batch_time / len(batch_prompts),
}
# Add task_id if available
if "task_id" in meta:
result["task_id"] = meta["task_id"]
# Add metrics if label available
if "label" in meta:
label = meta["label"]
result["correct_answer"] = label
result["is_correct"] = predicted_answer == label
# Update counts for accuracy
total_count += 1
if result["is_correct"]:
correct_count += 1
# Calculate perplexity if possible
if hasattr(self.model_handler, "calculate_perplexity"):
prompt = batch_prompts[j]
perplexity = self.model_handler.calculate_perplexity(prompt, response)
result["perplexity"] = perplexity
perplexities.append(perplexity)
results.append(result)
# Calculate aggregate metrics
summary_metrics = {}
if total_count > 0:
summary_metrics["accuracy"] = correct_count / total_count
summary_metrics["correct_count"] = correct_count
summary_metrics["total_count"] = total_count
if perplexities:
summary_metrics["avg_perplexity"] = sum(perplexities) / len(perplexities)
summary_metrics["min_perplexity"] = min(perplexities)
summary_metrics["max_perplexity"] = max(perplexities)
# Restore original prompt type if it was overridden
if original_prompt_type is not None:
self.prompt_creator.set_prompt_type(original_prompt_type)
self.response_parser = ResponseParser.from_prompt_type(original_prompt_type)
return results, summary_metrics
def evaluate_dataset(self, dataset, temperature=0.7, max_tokens=1024, num_examples=None,
verbose=True, prompt_type=None, batch_size=4, log_to_wandb=False):
"""Inference on a whole dataset with metrics calculation"""
# Allow overriding the prompt type for this evaluation
original_prompt_type = self.prompt_creator.prompt_type
if prompt_type is not None:
self.prompt_creator.set_prompt_type(prompt_type)
self.response_parser = ResponseParser.from_prompt_type(prompt_type)
# Select subset if specified
if num_examples is not None:
dataset = dataset.select(range(min(num_examples, len(dataset))))
results = []
correct_count = 0
total_count = 0
perplexities = []
# Process examples in batches
for i in range(0, len(dataset), batch_size):
batch_examples = dataset[i:i+batch_size]
if verbose:
batch_desc = f"Batch {i//batch_size + 1}/{(len(dataset) + batch_size - 1) // batch_size}"
print(f"\nProcessing {batch_desc} with {len(batch_examples)} examples...")
# Infer batch
batch_results, batch_metrics = self.infer_batch(
examples=batch_examples,
temperature=temperature,
max_tokens=max_tokens,
batch_size=batch_size
)
# Update metrics
results.extend(batch_results)
if "correct_count" in batch_metrics:
correct_count += batch_metrics["correct_count"]
total_count += batch_metrics["total_count"]
if verbose:
batch_accuracy = batch_metrics["accuracy"]
overall_accuracy = correct_count / total_count
print(f"Batch accuracy: {batch_accuracy:.2%}, Overall: {overall_accuracy:.2%} ({correct_count}/{total_count})")
# Collect perplexities
if "avg_perplexity" in batch_metrics:
for result in batch_results:
if "perplexity" in result:
perplexities.append(result["perplexity"])
# Calculate final accuracy
accuracy = correct_count / total_count if total_count > 0 else 0.0
if verbose:
prompt_type_str = self.prompt_creator.prompt_type
print(f"\nFinal accuracy with {prompt_type_str} prompts: {accuracy:.2%} ({correct_count}/{total_count})")
if perplexities:
avg_perplexity = sum(perplexities) / len(perplexities)
print(f"Average perplexity: {avg_perplexity:.4f}")
# Prepare comprehensive summary
summary = {
"accuracy": accuracy,
"correct_count": correct_count,
"total_count": total_count,
"prompt_type": self.prompt_creator.prompt_type,
"results": results,
}
# Add perplexity metrics if available
if perplexities:
summary["avg_perplexity"] = sum(perplexities) / len(perplexities)
summary["min_perplexity"] = min(perplexities)
summary["max_perplexity"] = max(perplexities)
# Log results to wandb if requested
if log_to_wandb and wandb.run is not None:
metrics = {
"test/accuracy": accuracy,
"test/correct_count": correct_count,
"test/total_count": total_count,
}
if perplexities:
metrics["test/avg_perplexity"] = summary["avg_perplexity"]
metrics["test/min_perplexity"] = summary["min_perplexity"]
metrics["test/max_perplexity"] = summary["max_perplexity"]
wandb.log(metrics)
# Create a table of results for visualization if task_id exists
if "task_id" in dataset.features:
columns = ["task_id", "question", "correct_answer", "predicted_answer", "is_correct"]
table = wandb.Table(columns=columns)
for res in results[:min(100, len(results))]:
table.add_data(
res.get("task_id", "unknown"),
res["question"][:100] + "...",
res.get("correct_answer", ""),
res.get("predicted_answer", ""),
res.get("is_correct", False)
)
wandb.log({"test_samples": table})
# Restore original prompt type
self.prompt_creator.set_prompt_type(original_prompt_type)
self.response_parser = ResponseParser.from_prompt_type(original_prompt_type)
return summary
def save_results(self, results, output_dir="./results"):
"""Save evaluation results to file"""
os.makedirs(output_dir, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
results_file = os.path.join(output_dir, f"results_{timestamp}.json")
# Create serializable results
serializable_results = {
"accuracy": results.get("accuracy", 0.0),
"correct_count": results.get("correct_count", 0),
"total_count": results.get("total_count", 0),
"timestamp": timestamp,
"prompt_type": results.get("prompt_type", "unknown"),
}
# Add perplexity metrics if available
if "avg_perplexity" in results:
serializable_results["avg_perplexity"] = results["avg_perplexity"]
serializable_results["min_perplexity"] = results["min_perplexity"]
serializable_results["max_perplexity"] = results["max_perplexity"]
# Process individual results
serializable_results["individual_results"] = []
for result in results["results"]:
# Skip perplexity in individual results to save space
result_copy = result.copy()
if "perplexity" in result_copy:
del result_copy["perplexity"]
# Convert choices if needed
choices = result_copy["choices"]
if not isinstance(choices, list):
try:
import ast
result_copy["choices"] = ast.literal_eval(choices)
except (SyntaxError, ValueError):
pass
serializable_results["individual_results"].append(result_copy)
# Save to file
with open(results_file, "w") as f:
import json
json.dump(serializable_results, f, indent=2)
print(f"Results saved to {results_file}")
return results_file
Quick Start
Here's a simple example of how to use the model:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
model_id = "tuandunghcmut/Qwen25_Coder_MultipleChoice"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
# Example question
question = "What is the correct way to open a file in Python for reading?"
choices = [
"open('file.txt', 'r')",
"file.open('file.txt', 'read')",
"read('file.txt')",
"File.open('file.txt')"
]
# Format the prompt
prompt = f"""
QUESTION:
{question}
CHOICES:
{chr(65 + i)}. {choice}
for i, choice in enumerate(choices)}
Answer with a single letter from A through {chr(65 + len(choices) - 1)} without any additional explanation or commentary.
"""
# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=10)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Model's answer: {response}")
Advanced Usage
Using the MultipleChoiceTester Framework
For more advanced usage, you can use the provided MultipleChoiceTester framework:
from save import QwenModelHandler, MultipleChoiceTester, PromptCreator
# Initialize the model handler
model_handler = QwenModelHandler(
model_name="tuandunghcmut/Qwen25_Coder_MultipleChoice",
max_seq_length=2048,
quantization="4bit",
device_map="auto"
)
# Create a prompt creator with YAML reasoning format
prompt_creator = PromptCreator(PromptCreator.YAML_REASONING)
# Initialize the tester
tester = MultipleChoiceTester(model_handler, prompt_creator=prompt_creator)
# Example question
example = {
"question": "What is the correct way to open a file in Python for reading?",
"choices": [
"open('file.txt', 'r')",
"file.open('file.txt', 'read')",
"read('file.txt')",
"File.open('file.txt')"
],
"answer": "A" # Optional ground truth
}
# Get prediction with reasoning
result = tester.infer_example(example, temperature=0.0001, stream=True)
print(f"Predicted answer: {result['predicted_answer']}")
print("Reasoning:")
print(result['reasoning'])
Batch Processing
You can also process multiple questions in batches:
# List of examples
examples = [
{
"question": "What is the correct way to open a file in Python for reading?",
"choices": ["open('file.txt', 'r')", "file.open('file.txt', 'read')", "read('file.txt')", "File.open('file.txt')"],
"answer": "A"
},
# Add more examples...
]
# Process batch
results, metrics = tester.infer_batch(examples, batch_size=4)
print(f"Batch accuracy: {metrics['accuracy']:.2%}")
Streaming Inference
The model supports streaming inference, which provides real-time output as the model generates its response. This is particularly useful for interactive applications and when you want to see the reasoning process in real-time.
Basic Streaming Usage
Here's how to use streaming inference:
# Initialize model handler and tester as before
model_handler = QwenModelHandler(
model_name="tuandunghcmut/Qwen25_Coder_MultipleChoice",
max_seq_length=2048
)
tester = MultipleChoiceTester(model_handler)
# Example with streaming
example = {
"question": "Which Python method is used to remove whitespace from both ends of a string?",
"choices": [
"strip()",
"trim()",
"clean()",
"remove_whitespace()"
],
"answer": "A"
}
# Enable streaming with stream=True
result = tester.infer_example(
example,
temperature=0.0001,
max_tokens=1024,
stream=True # Enable streaming
)
# The output will be printed in real-time as the model generates it
# You can also access the complete response after generation
print("\nFinal result:")
print(f"Predicted answer: {result['predicted_answer']}")
print("Complete reasoning:")
print(result['reasoning'])
Advanced Streaming Patterns
1. Custom Stream Processing
You can process the streamed output in custom ways:
def process_stream(streamer):
"""Custom stream processing function"""
collected_text = ""
for chunk in streamer:
# Process each chunk as it arrives
collected_text += chunk
# You can do custom processing here
# For example, parse partial YAML, update UI, etc.
yield chunk, collected_text
# Use custom stream processing
result = tester.infer_example(
example,
temperature=0.0001,
stream=True
)
# Process the stream with custom logic
for chunk, full_text in process_stream(result['stream']):
# Do something with each chunk
print(f"Chunk: {chunk}")
print(f"Full text so far: {full_text}")
2. YAML Streaming with Real-time Parsing
When using YAML reasoning format, you can parse the output as it streams:
import yaml
from io import StringIO
def parse_yaml_stream(streamer):
"""Parse YAML content as it streams"""
buffer = StringIO()
for chunk in streamer:
buffer.write(chunk)
try:
# Try to parse the current buffer as YAML
yaml_content = yaml.safe_load(buffer.getvalue())
if yaml_content:
yield chunk, yaml_content
except yaml.YAMLError:
# Not enough content for valid YAML yet
continue
# Use YAML streaming with parsing
result = tester.infer_example(
example,
temperature=0.0001,
prompt_type=PromptCreator.YAML_REASONING,
stream=True
)
# Process YAML content as it streams
for chunk, yaml_content in parse_yaml_stream(result['stream']):
if isinstance(yaml_content, dict):
# Access YAML fields as they become available
if 'understanding' in yaml_content:
print(f"Understanding: {yaml_content['understanding']}")
if 'reasoning' in yaml_content:
print(f"Reasoning: {yaml_content['reasoning']}")
if 'answer' in yaml_content:
print(f"Answer: {yaml_content['answer']}")
3. Streaming with Progress Tracking
You can track generation progress and timing:
import time
def stream_with_progress(streamer):
"""Stream with progress tracking"""
start_time = time.time()
tokens_generated = 0
for chunk in streamer:
tokens_generated += len(chunk.split())
elapsed = time.time() - start_time
tokens_per_second = tokens_generated / elapsed if elapsed > 0 else 0
yield {
'chunk': chunk,
'tokens': tokens_generated,
'tokens_per_second': tokens_per_second,
'elapsed': elapsed
}
# Use streaming with progress tracking
result = tester.infer_example(
example,
temperature=0.0001,
stream=True
)
for progress in stream_with_progress(result['stream']):
print(f"Generated {progress['tokens']} tokens "
f"({progress['tokens_per_second']:.2f} tokens/sec)")
print(f"Chunk: {progress['chunk']}")
Implementation Details
The streaming implementation uses Unsloth's optimized inference with the following key features:
Efficient Token Generation
- Uses Unsloth's
FastLanguageModelfor optimized inference - Implements streaming using
TextIteratorStreamer - Supports both greedy and temperature-based sampling
- Uses Unsloth's
Memory Management
- Streams tokens without storing the entire response in memory
- Efficiently handles long responses
- Supports batch processing with streaming
Performance Optimizations
- Uses
use_cache=Truefor faster generation - Implements
min_psampling for better quality - Supports 4-bit quantization for reduced memory usage
- Uses
Error Handling
- Gracefully handles streaming interruptions
- Provides partial results if generation is interrupted
- Maintains context for resumed generation
The streaming output will show the model's reasoning process in real-time, including:
- Understanding of the question
- Analysis of each option
- Step-by-step reasoning
- Final conclusion
- Answer selection
This is particularly useful for:
- Debugging model behavior
- Creating interactive demos
- Understanding the model's reasoning process
- Providing immediate feedback to users
- Building real-time applications
Model Features
- YAML-Based Reasoning: The model provides structured reasoning in YAML format
- Multiple Prompt Types: Supports both basic and YAML-formatted reasoning prompts
- Batch Processing: Efficiently process multiple questions at once
- Performance Metrics: Tracks accuracy, perplexity, and response times
- Streaming Support: Real-time output streaming for interactive use
License
This project is licensed under the MIT License - see the LICENSE file for details.
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