Instructions to use t83714/llama-3.1-8b-instruct-limo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use t83714/llama-3.1-8b-instruct-limo with Transformers:

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

pipe = pipeline("text-generation", model="t83714/llama-3.1-8b-instruct-limo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("t83714/llama-3.1-8b-instruct-limo")
model = AutoModelForCausalLM.from_pretrained("t83714/llama-3.1-8b-instruct-limo", device_map="auto")
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 t83714/llama-3.1-8b-instruct-limo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "t83714/llama-3.1-8b-instruct-limo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "t83714/llama-3.1-8b-instruct-limo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/t83714/llama-3.1-8b-instruct-limo

SGLang

How to use t83714/llama-3.1-8b-instruct-limo 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 "t83714/llama-3.1-8b-instruct-limo" \
    --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": "t83714/llama-3.1-8b-instruct-limo",
		"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 "t83714/llama-3.1-8b-instruct-limo" \
        --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": "t83714/llama-3.1-8b-instruct-limo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use t83714/llama-3.1-8b-instruct-limo with Docker Model Runner:
```
docker model run hf.co/t83714/llama-3.1-8b-instruct-limo
```

llama-3.1-8b-instruct-limo-lora

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct model. The fine-tuning was performed using Low-Rank Adaptation (LoRA) on the LIMO dataset to enhance the model's reasoning capabilities, based on the work in the paper: LIMO: Less is More for Reasoning.

This repo contains the merged model weights. The LoRA adapter version can be found from here.

Model description

Base Model: meta-llama/Llama-3.1-8B-Instruct
Fine-Tuning Dataset: GAIR/LIMO
Fine-Tuning Method: Low-Rank Adaptation (LoRA)
Library Used: peft
License: Apache 2.0

Usage

To utilize this model for text generation tasks, follow the steps below:

Installation

Ensure you have the necessary libraries installed:

pip install torch transformers

Generating Text

from transformers import AutoModelForCausalLM, AutoTokenizer
  
model_name = "t83714/llama-3.1-8b-instruct-limo"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How much is (2+5)x5/7"

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

# Generate the output
output = model.generate(**inputs, max_length=8000)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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
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
num_epochs: 15

Framework versions

PEFT 0.12.0
Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Acknowledgment

This model is trained based on the work of Ye et al. (2025). If you use this model, please also consider citing their paper:

@misc{ye2025limoreasoning,
      title={LIMO: Less is More for Reasoning}, 
      author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
      year={2025},
      eprint={2502.03387},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.03387}, 
}