| --- |
| 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] |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **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. --> |
|
|
| [More Information Needed] |
|
|
| ## Bias, Risks, and Limitations |
|
|
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
|
| [More Information Needed] |
|
|
| ### Recommendations |
|
|
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
|
|
| 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 |
|
|
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
|
| [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] |
|
|
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
|
| [More Information Needed] |
|
|
| ## Evaluation |
|
|
| <!-- This section describes the evaluation protocols and provides the results. --> |
|
|
| ### Testing Data, Factors & Metrics |
|
|
| #### Testing Data |
|
|
| <!-- This should link to a Dataset Card if possible. --> |
|
|
| [More Information Needed] |
|
|
| #### Factors |
|
|
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
| [More Information Needed] |
|
|
| #### Metrics |
|
|
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
| [More Information Needed] |
|
|
| ### Results |
|
|
| [More Information Needed] |
|
|
| #### Summary |
|
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|
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|
|
| ## Model Examination [optional] |
|
|
| <!-- Relevant interpretability work for the model goes here --> |
|
|
| [More Information Needed] |
|
|
| ## 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 |
|
|
| [More Information Needed] |
|
|
| #### Hardware |
|
|
| [More Information Needed] |
|
|
| #### Software |
|
|
| [More Information Needed] |
|
|
| ## Citation [optional] |
|
|
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
| **BibTeX:** |
|
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| [More Information Needed] |
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| **APA:** |
|
|
| [More Information Needed] |
|
|
| ## Glossary [optional] |
|
|
| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
|
|
| [More Information Needed] |
|
|
| ## More Information [optional] |
|
|
| [More Information Needed] |
|
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| ## Model Card Authors [optional] |
|
|
| [More Information Needed] |
|
|
| ## Model Card Contact |
|
|
| [More Information Needed] |
| ### Framework versions |
|
|
| - PEFT 0.17.1 |
