Instructions to use apoorva2311/Limopro-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apoorva2311/Limopro-model with Transformers:

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

pipe = pipeline("text-generation", model="apoorva2311/Limopro-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("apoorva2311/Limopro-model")
model = AutoModelForCausalLM.from_pretrained("apoorva2311/Limopro-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use apoorva2311/Limopro-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apoorva2311/Limopro-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apoorva2311/Limopro-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/apoorva2311/Limopro-model

SGLang

How to use apoorva2311/Limopro-model 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 "apoorva2311/Limopro-model" \
    --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": "apoorva2311/Limopro-model",
		"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 "apoorva2311/Limopro-model" \
        --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": "apoorva2311/Limopro-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use apoorva2311/Limopro-model with Docker Model Runner:
```
docker model run hf.co/apoorva2311/Limopro-model
```

qwen_limopro_output

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct on the acereason_limopro_15k dataset.

Model description

Qwen-LIMOPro-Output builds on Qwen2.5-3B-Instruct, a general-purpose instruction-following model.
Through supervised fine-tuning (SFT) on the AceReason-LIMOPro subset, the model was optimized to improve mathematical and logical reasoning.

The subset emphasizes:

Challenging problems (high reasoning complexity)
Unfamiliar examples (low similarity to base model’s prior knowledge)
Diverse coverage across topics (via K-means clustering of embeddings)

This approach helps strengthen reasoning depth while minimizing overfitting and catastrophic forgetting often seen in small-scale SFT.

Intended uses & limitations

Use Cases

Step-by-step mathematical reasoning
Problem-solving and scientific question answering
Educational or research applications for reasoning-focused LLMs

Limitations

Not suitable for general open-domain QA or creative text generation (model over-specialized toward reasoning tasks)
Some degradation observed on general benchmarks (e.g., MMLU)
May over-explain or generate verbose reasoning chains for simple prompts

Training and evaluation data

Training Dataset: apoorva2311/TML-limopro
- 15K examples sampled from AceReason-1.1-SFT (100K total)
- Selected via difficulty and model familiarity scoring
- Ensures high reasoning quality and topic diversity
Evaluation Benchmarks:
- AIME24 / AIME25
- MATH-500
- GPQA-Diamond
- LiveCodeBench (LCB)
- MMLU-Redux-2

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3

Average Results (vs. Baseline Qwen2.5-3B-Instruct)

Benchmark	Baseline	Random SFT	LIMOPro SFT
MATH-500	0.6340	0.5060	0.5600
AIME25	0.0000	0.0333	0.1000
MMLU (Acc)	0.6393	0.2356	0.4404

The LIMOPro variant achieved +22% higher overall accuracy compared to random fine-tuning, with improved stability and reasoning alignment.

Training results

Training Configuration

Environment: Virginia Tech ARC (Tinkercliffs, 4×L40S GPUs)
Framework: LLaMA-Factory
Fine-tuning Type: Full-parameter SFT
Precision: BF16
Deepspeed Config: ds_z3_offload_config.json

Hyperparameters

Parameter	Value
Learning Rate	5e-6
Batch Size	1
Gradient Accumulation	16
Total Batch Size	64
Epochs	3
Scheduler	Cosine
Warmup Ratio	0.05
Optimizer	AdamW (β₁=0.9, β₂=0.999, ε=1e-8)
Seed	42

The model exhibits stronger structured reasoning and clearer intermediate step articulation.
Slightly reduced performance on broad factual tasks (trade-off for reasoning specialization).
Fine-tuning on the LIMOPro-selected subset led to smoother loss curves and better convergence stability compared to random sampling.

Framework versions

Transformers 4.56.2
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: -

Safetensors

Model size

242k params

Tensor type

BF16

Model tree for apoorva2311/Limopro-model

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1339)

this model