Instructions to use Hplm/dora_llama_model_1820_1850 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hplm/dora_llama_model_1820_1850 with Transformers:

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

pipe = pipeline("text-generation", model="Hplm/dora_llama_model_1820_1850")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hplm/dora_llama_model_1820_1850")
model = AutoModelForCausalLM.from_pretrained("Hplm/dora_llama_model_1820_1850")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Hplm/dora_llama_model_1820_1850 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hplm/dora_llama_model_1820_1850"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hplm/dora_llama_model_1820_1850",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hplm/dora_llama_model_1820_1850

SGLang

How to use Hplm/dora_llama_model_1820_1850 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 "Hplm/dora_llama_model_1820_1850" \
    --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": "Hplm/dora_llama_model_1820_1850",
		"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 "Hplm/dora_llama_model_1820_1850" \
        --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": "Hplm/dora_llama_model_1820_1850",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hplm/dora_llama_model_1820_1850 with Docker Model Runner:
```
docker model run hf.co/Hplm/dora_llama_model_1820_1850
```

Improve model card: Add description, link to paper and code, pipeline tag, and historical linguistics tag

by nielsr HF Staff - opened Apr 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+12

-48

nielsr

Apr 11, 2025

This PR enriches the model card by:

Adding a concise model description.
Linking to the paper.
Linking to the GitHub repository.
Adding the text-generation pipeline tag to ensure the model appears in the correct category.
Adding the historical-linguistics tag.

Improve model card: Add description, link to paper and code, pipeline tag, and historical linguistics tag869574ef

efittschen

Historical Perspectival LM org Apr 17, 2025

Thank you, the changes were added, but in a different commit

efittschen changed pull request status to closed Apr 17, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment