Instructions to use Lapisbird/Llama-adaLR-model-cot_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lapisbird/Llama-adaLR-model-cot_sft with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lapisbird/Llama-adaLR-model-cot_sft")
model = AutoModelForCausalLM.from_pretrained("Lapisbird/Llama-adaLR-model-cot_sft", 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 Lapisbird/Llama-adaLR-model-cot_sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Lapisbird/Llama-adaLR-model-cot_sft

SGLang

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

Docker Model Runner
How to use Lapisbird/Llama-adaLR-model-cot_sft with Docker Model Runner:
```
docker model run hf.co/Lapisbird/Llama-adaLR-model-cot_sft
```

Improve model card: Add pipeline tag, paper link, code link, description, and usage

by nielsr HF Staff - opened Nov 27, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+44

-4

nielsr

Nov 27, 2025

This PR significantly enhances the model card by:

Linking to the paper: Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning.
Adding a link to the official GitHub repository: https://github.com/apning/adaptive-latent-reasoning.
Including a concise description of the model based on its abstract.
Adding the pipeline_tag: text-generation to improve discoverability on the Hugging Face Hub.
Adding relevant additional tags: reinforcement-learning, latent-reasoning, and math.
Providing a sample Python code snippet from the GitHub README for quick model loading and usage.
Adding a citation section.

Please review and merge if these improvements are satisfactory!

Improve model card: Add pipeline tag, paper link, code link, description, and usage47cf1ac1

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