Instructions to use ivanzhouyq/levanter-backpack-1b-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ivanzhouyq/levanter-backpack-1b-100k with Transformers:

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

pipe = pipeline("text-generation", model="ivanzhouyq/levanter-backpack-1b-100k", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ivanzhouyq/levanter-backpack-1b-100k", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ivanzhouyq/levanter-backpack-1b-100k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ivanzhouyq/levanter-backpack-1b-100k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ivanzhouyq/levanter-backpack-1b-100k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ivanzhouyq/levanter-backpack-1b-100k

SGLang

How to use ivanzhouyq/levanter-backpack-1b-100k 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 "ivanzhouyq/levanter-backpack-1b-100k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ivanzhouyq/levanter-backpack-1b-100k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ivanzhouyq/levanter-backpack-1b-100k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ivanzhouyq/levanter-backpack-1b-100k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ivanzhouyq/levanter-backpack-1b-100k with Docker Model Runner:
```
docker model run hf.co/ivanzhouyq/levanter-backpack-1b-100k
```

Configuration Parsing Warning:In config.json: "architectures" must be an array

How to Get Started with the Model

Please install transformers, safetensors and torch to use this model.

pip install transformers safetensors torch

Run the following Python code:

import torch
import transformers
from transformers import AutoModelForCausalLM


model_id = "ivanzhouyq/levanter-backpack-1b-100k"
config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=True)
torch_model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    config=config, 
    trust_remote_code=True
)
torch_model.eval()

input = torch.randint(0, 50264, (1, 512), dtype=torch.long)
torch_out = torch_model(input, position_ids=None,)
torch_out = torch.nn.functional.softmax(torch_out.logits, dim=-1)
print(torch_out.shape)

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

F32

ivanzhouyq
/

levanter-backpack-1b-100k

How to Get Started with the Model

Dataset used to train ivanzhouyq/levanter-backpack-1b-100k