Instructions to use Honkware/Manticore-13b-Landmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Honkware/Manticore-13b-Landmark with Transformers:

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

pipe = pipeline("text-generation", model="Honkware/Manticore-13b-Landmark", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Honkware/Manticore-13b-Landmark", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Honkware/Manticore-13b-Landmark", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Honkware/Manticore-13b-Landmark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Honkware/Manticore-13b-Landmark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Honkware/Manticore-13b-Landmark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Honkware/Manticore-13b-Landmark

SGLang

How to use Honkware/Manticore-13b-Landmark 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 "Honkware/Manticore-13b-Landmark" \
    --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": "Honkware/Manticore-13b-Landmark",
		"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 "Honkware/Manticore-13b-Landmark" \
        --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": "Honkware/Manticore-13b-Landmark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Honkware/Manticore-13b-Landmark with Docker Model Runner:
```
docker model run hf.co/Honkware/Manticore-13b-Landmark
```

Manticore-13b-Landmark

Key Features

Landmark Attention
Large Context Size (~18k)

Composition

Manticore-13b-Landmark is a blend of:

Using Oobabooga

Trust Remote Code - (Enabled)
Add the bos_token to the beginning of prompts - (Disabled)
Truncate the prompt up to this length - (Increased)

Landmark Training Code

See GitHub for the training code.

Downloads last month: 14

Paper for Honkware/Manticore-13b-Landmark

Landmark Attention: Random-Access Infinite Context Length for Transformers

Paper • 2305.16300 • Published May 25, 2023