Instructions to use ComputationalLinguisticsLab/LexisLM-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ComputationalLinguisticsLab/LexisLM-1B with Transformers:

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

pipe = pipeline("text-generation", model="ComputationalLinguisticsLab/LexisLM-1B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ComputationalLinguisticsLab/LexisLM-1B")
model = AutoModelForCausalLM.from_pretrained("ComputationalLinguisticsLab/LexisLM-1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ComputationalLinguisticsLab/LexisLM-1B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ComputationalLinguisticsLab/LexisLM-1B

SGLang

How to use ComputationalLinguisticsLab/LexisLM-1B 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 "ComputationalLinguisticsLab/LexisLM-1B" \
    --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": "ComputationalLinguisticsLab/LexisLM-1B",
		"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 "ComputationalLinguisticsLab/LexisLM-1B" \
        --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": "ComputationalLinguisticsLab/LexisLM-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ComputationalLinguisticsLab/LexisLM-1B with Docker Model Runner:
```
docker model run hf.co/ComputationalLinguisticsLab/LexisLM-1B
```

LexisLM-1B

LexisLM-1B is a Greek language, decoder-only model for text completion. It was pretrained for 335,000 steps on approximately 22 billion tokens from FineWeb2 and Greek Wikipedia. This release is the base model; it has not been instruction tuned for chat or question answering.

Model details

Property	Value
Architecture	Llama-compatible causal language model
Transformer layers	20
Hidden size	2,048
Attention heads	16
Context length	4,096 tokens
Vocabulary size	32,004 tokens

The repository includes the model weights in safetensors format, the configuration, and the tokenizer. It can be loaded with Hugging Face Transformers.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ComputationalLinguisticsLab/LexisLM-1B"  # Or a local path to hf_pretrained
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

# Pretraining documents were separated by <eos>. Start a fresh document with it.
prompt = "Η Ελλάδα είναι μια χώρα"
prompt_ids = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids
eos_id = torch.tensor([[tokenizer.eos_token_id]])
input_ids = torch.cat([eos_id, prompt_ids], dim=1).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.pad_token_id,
    )

print(tokenizer.decode(output_ids[0, input_ids.shape[1] :], skip_special_tokens=True))

Install torch, transformers, and accelerate to run this example. Keep the prompt and generated tokens within the 4,096-token context window.

Evaluation results

The following results were reported with 0 few-shot examples. Scores are shown as percentages. ARC uses normalized accuracy; the other benchmarks use their reported accuracy or multiple-choice metrics.

Benchmark	Metric	Score	Questions
ARC-Challenge	Normalized accuracy	25.34%	1,168
ARC-Easy	Normalized accuracy	33.38%	2,376
MMLU	Accuracy	23.15%	14,042
TruthfulQA	MC1	24.60%	817
TruthfulQA	MC2	43.49%	817

Intended use and limitations

LexisLM-1B is intended for experimenting with Greek text generation and as a starting point for further fine-tuning. Because it is a base model, chat-style instructions may not produce reliable answers. Generated text can be inaccurate or reflect biases in the training data; review it before using it in consequential settings.

License

LexisLM-1B model weights are released under Apache-2.0. Training data included FineWeb2 (HuggingFaceFW), available under ODC-By-1.0. Dataset and underlying content rights remain with their respective holders.

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Safetensors

Model size

1B params

Tensor type

BF16