Finetuned Historical Models
Collection
Collection of the finetuned historical models. • 5 items • Updated
How to use Hplm/dora_llama_model_1910_1940 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Hplm/dora_llama_model_1910_1940") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Hplm/dora_llama_model_1910_1940")
model = AutoModelForCausalLM.from_pretrained("Hplm/dora_llama_model_1910_1940")How to use Hplm/dora_llama_model_1910_1940 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hplm/dora_llama_model_1910_1940"
# 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_1910_1940",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Hplm/dora_llama_model_1910_1940
How to use Hplm/dora_llama_model_1910_1940 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Hplm/dora_llama_model_1910_1940" \
--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_1910_1940",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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_1910_1940" \
--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_1910_1940",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Hplm/dora_llama_model_1910_1940 with Docker Model Runner:
docker model run hf.co/Hplm/dora_llama_model_1910_1940
This model was finetuned using DoRA adapters, from the Llama3 8B model.
It was finetuned on 10M words from the Gutenberg Corpus attributed to the time period 1910 - 1940.
Load the model like this:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Hplm/dora_llama_model_1910_1940", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("Hplm/dora_llama_model_1910_1940")
Built with Meta Llama 3, and under the meta-llama licence.
@article{fittschen_diachroniclanguagemodels_2025,
title = {Pretraining Language Models for Diachronic Linguistic Change Discovery},
author = {Fittschen, Elisabeth and Li, Sabrina and Lippincott, Tom and Choshen, Leshem and Messner, Craig},
year = {2025},
month = apr,
eprint = {2504.05523},
primaryclass = {cs.CL},
publisher = {arXiv},
doi = {10.48550/arXiv.2504.05523},
url = {https://arxiv.org/abs/2504.05523},
urldate = {2025-04-14},
archiveprefix = {arXiv},
journal = {arxiv:2504.05523[cs.CL]}
}