Instructions to use SaffalPoosh/reasoning_cpp_llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SaffalPoosh/reasoning_cpp_llm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "SaffalPoosh/reasoning_cpp_llm")

Transformers

How to use SaffalPoosh/reasoning_cpp_llm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SaffalPoosh/reasoning_cpp_llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SaffalPoosh/reasoning_cpp_llm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SaffalPoosh/reasoning_cpp_llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SaffalPoosh/reasoning_cpp_llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SaffalPoosh/reasoning_cpp_llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SaffalPoosh/reasoning_cpp_llm

SGLang

How to use SaffalPoosh/reasoning_cpp_llm 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 "SaffalPoosh/reasoning_cpp_llm" \
    --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": "SaffalPoosh/reasoning_cpp_llm",
		"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 "SaffalPoosh/reasoning_cpp_llm" \
        --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": "SaffalPoosh/reasoning_cpp_llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use SaffalPoosh/reasoning_cpp_llm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SaffalPoosh/reasoning_cpp_llm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SaffalPoosh/reasoning_cpp_llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SaffalPoosh/reasoning_cpp_llm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SaffalPoosh/reasoning_cpp_llm",
    max_seq_length=2048,
)

Docker Model Runner
How to use SaffalPoosh/reasoning_cpp_llm with Docker Model Runner:
```
docker model run hf.co/SaffalPoosh/reasoning_cpp_llm
```

Model Card for Model ID

Model Card for SaffalPoosh/reasoning_cpp_llm

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

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

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:

Problem analysis and reasoning
Algorithm explanation
Complete C++ implementation
Time and space complexity analysis

Requirements

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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Language(s) (NLP): [More Information Needed]
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Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
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Framework versions

PEFT 0.17.1

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Model tree for SaffalPoosh/reasoning_cpp_llm

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit

Adapter

(12)

this model

Dataset used to train SaffalPoosh/reasoning_cpp_llm

Paper for SaffalPoosh/reasoning_cpp_llm

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 48