README.md

---
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
license: apache-2.0
datasets:
- open-r1/codeforces-cots
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



# Model Card for SaffalPoosh/reasoning_cpp_llm

<!-- Provide a quick summary of what the model is/does. -->

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. Problem analysis and reasoning
2. Algorithm explanation
3. Complete C++ implementation
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

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.17.1