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SaffalPoosh/reasoning_cpp_llm

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Model Card for SaffalPoosh/reasoningcppllm

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This is a QLoRA adapter trained on C++ coding tasks and designed for reasoning-based code generation. The model specializes in solving algorithmic problems with step-by-step reasoning and generating optimized C++ solutions.

Example Usage

Problem Example

python
example_problem = """
A robot is situated at the top-left corner of an m x n grid. The robot can only move either down or right at any point in time. It wants to reach the bottom-right corner of the grid. Some cells in the grid are blocked by obstacles. How many unique paths can the robot take to reach the destination?

Constraints:
Time limit per test: 2.0 seconds
Memory limit per test: 256.0 megabytes
1 ≤ m, n ≤ 100
Grid cells are either 0 (empty) or 1 (obstacle).

Input Format:
The first line contains two integers m and n — the dimensions of the grid.
The next m lines each contain n integers (0 or 1) representing the grid.

Output Format:
Print a single integer — the number of unique paths.

Example:
Input:
3 3
0 0 0
0 1 0
0 0 0
"""

Model Loading and Inference

python
from unsloth import FastLanguageModel
from transformers import TextStreamer
from transformers import TextIteratorStreamer
from threading import Thread

# Model configuration
model_path = "SaffalPoosh/reasoning_cpp_llm"
max_seq_length = 16000 
dtype = None 
load_in_4bit = True 

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_path,
    max_seq_length=max_seq_length, 
    dtype=dtype,        
    load_in_4bit=load_in_4bit,
    local_files_only=False
)

# This will download the base model and then patch by applying the LoRA adapters
FastLanguageModel.for_inference(model)

# Prepare Input Data
input_text = example_problem
inputs = tokenizer(input_text, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}

# Initialize the text streamer
text_streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=False)

# Perform Inference with streaming
stream_catcher = Thread(
    target=model.generate, 
    kwargs={
        **inputs, 
        "do_sample": True, 
        "streamer": text_streamer, 
        "max_new_tokens": 10000
    }
)

stream_catcher.start()

# Stream output to console and file
with open("output.txt", "w") as f:
    for token in text_streamer:
        print(token, end="", flush=True)
        f.write(token)

stream_catcher.join()

Model Details

  • —Model Type: QLoRA Fine-tuned Language Model
  • —Base Model: [Specify base model if known]
  • —Training Focus: C++ algorithmic problem solving with reasoning
  • —Max Sequence Length: 16,000 tokens
  • —Quantization: 4-bit loading supported
  • —Hardware Requirements: CUDA-compatible GPU recommended

Training Details

  • —Training Method: QLoRA (Quantized Low-Rank Adaptation)
  • —Dataset: C++ coding tasks with reasoning annotations
  • —Task Type: Code generation with step-by-step reasoning
  • —Optimization: Focused on algorithmic problem solving

Usage Notes

  • —The model generates reasoning-based solutions for C++ programming problems
  • —Supports streaming inference for real-time output
  • —The output.txt file contains the complete generated solution
  • —Designed to handle competitive programming style problems with constraints

Output Format

The model typically generates:

  1. 1.Problem analysis and reasoning
  2. 2.Algorithm explanation
  3. 3.Complete C++ implementation
  4. 4.Time and space complexity analysis

Requirements

python
pip install unsloth transformers torch

Hardware Requirements

  • —GPU: CUDA-compatible GPU (recommended)
  • —Memory: Sufficient VRAM for 4-bit quantized model
  • —Storage: Space for base model download and adapter weights
  • —

Model Details

Model Description

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Uses

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Direct Use

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Recommendations

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How to Get Started with the Model

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Training Details

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

  • —PEFT 0.17.1