Instructions to use hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT with Transformers:

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

pipe = pipeline("text-generation", model="hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT")
model = AutoModelForCausalLM.from_pretrained("hsaest/Llama-3.1-8B-Instruct-travelplanner-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 hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hsaest/Llama-3.1-8B-Instruct-travelplanner-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": "hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT

SGLang

How to use hsaest/Llama-3.1-8B-Instruct-travelplanner-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 "hsaest/Llama-3.1-8B-Instruct-travelplanner-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": "hsaest/Llama-3.1-8B-Instruct-travelplanner-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 "hsaest/Llama-3.1-8B-Instruct-travelplanner-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": "hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT with Docker Model Runner:
```
docker model run hf.co/hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

	Commonsense (Micro)	Commonsense (Macro)	Hard (Micro)	Hard (Macro)	Final Pass Rate
Direct Prompting
Llama3.1-8B	60.1	0.0	7.9	2.8	0.0
Qwen2-7B	49.9	1.1	2.1	0.0	0.0
Fine-tuning
Llama3.1-8B	78.3	17.8	19.3	6.1	3.8
Qwen2-7B	59.0	0.6	0.2	0.0	0.0

If our related resources prove valuable to your research, we kindly ask for a citation.

@article{xie2024revealing,
  title={Revealing the Barriers of Language Agents in Planning},
  author={Xie, Jian and Zhang, Kexun and Chen, Jiangjie and Yuan, Siyu and Zhang, Kai and Zhang, Yikai and Li, Lei and Xiao, Yanghua},
  journal={arXiv preprint arXiv:2410.12409},
  year={2024}
}

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Safetensors

Model size

8B params

Tensor type

F32

Model tree for hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT

Quantizations

2 models

Paper for hsaest/Llama-3.1-8B-Instruct-travelplanner-SFT

Revealing the Barriers of Language Agents in Planning

Paper • 2410.12409 • Published Oct 16, 2024 • 27