Instructions to use allenai/intent-aware-lfqa-llama3-8b-intent-implicit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/intent-aware-lfqa-llama3-8b-intent-implicit with Transformers:

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

pipe = pipeline("text-generation", model="allenai/intent-aware-lfqa-llama3-8b-intent-implicit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/intent-aware-lfqa-llama3-8b-intent-implicit")
model = AutoModelForCausalLM.from_pretrained("allenai/intent-aware-lfqa-llama3-8b-intent-implicit", 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 allenai/intent-aware-lfqa-llama3-8b-intent-implicit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/intent-aware-lfqa-llama3-8b-intent-implicit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/intent-aware-lfqa-llama3-8b-intent-implicit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/intent-aware-lfqa-llama3-8b-intent-implicit

SGLang

How to use allenai/intent-aware-lfqa-llama3-8b-intent-implicit 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 "allenai/intent-aware-lfqa-llama3-8b-intent-implicit" \
    --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": "allenai/intent-aware-lfqa-llama3-8b-intent-implicit",
		"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 "allenai/intent-aware-lfqa-llama3-8b-intent-implicit" \
        --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": "allenai/intent-aware-lfqa-llama3-8b-intent-implicit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/intent-aware-lfqa-llama3-8b-intent-implicit with Docker Model Runner:
```
docker model run hf.co/allenai/intent-aware-lfqa-llama3-8b-intent-implicit
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model

A distillation model checkpoint. For more details on intent aware training please read our paper!

Results

Will be updated soon.

Intended uses & limitations

This model is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Training

The script used to train this model can be found here.

Citation

@article{zhaoimproving,
  title={Improving Attributed Long-form Question Answering with Intent Awareness},
  author={Zhao, Xinran and Naik, Aakanksha and DeYoung, Jay and Chang, Joseph Chee and Hwang, Jena D and Wu, Tongshuang and Kishore, Varsha},
  journal={The Fourteenth International Conference on Learning Representations},
  year={2026}
}