Instructions to use GenerTeam/GENERator-eukaryote-3b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenerTeam/GENERator-eukaryote-3b-base with Transformers:

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

pipe = pipeline("text-generation", model="GenerTeam/GENERator-eukaryote-3b-base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenerTeam/GENERator-eukaryote-3b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-eukaryote-3b-base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GenerTeam/GENERator-eukaryote-3b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenerTeam/GENERator-eukaryote-3b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenerTeam/GENERator-eukaryote-3b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GenerTeam/GENERator-eukaryote-3b-base

SGLang

How to use GenerTeam/GENERator-eukaryote-3b-base 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 "GenerTeam/GENERator-eukaryote-3b-base" \
    --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": "GenerTeam/GENERator-eukaryote-3b-base",
		"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 "GenerTeam/GENERator-eukaryote-3b-base" \
        --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": "GenerTeam/GENERator-eukaryote-3b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GenerTeam/GENERator-eukaryote-3b-base with Docker Model Runner:
```
docker model run hf.co/GenerTeam/GENERator-eukaryote-3b-base
```

Add link to Github repository

by nielsr HF Staff - opened Apr 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -1,18 +1,21 @@
 ---
 license: mit
 pipeline_tag: text-generation
 tags:
 - biology
 - genomics
 - long-context
-library_name: transformers
 ---
 # GENERator-eukaryote-3b-base model
 ## Abouts
 In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 3B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. The extensive and diverse pre-training data endow the GENERator with enhanced understanding and generation capabilities across various organisms.
-For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://huggingface.co/GenerTeam).
 ## How to use
 ### Simple example1:  generation
@@ -72,7 +75,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 # Load the tokenizer and model.
 tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-3b-base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-3b-base")
 config = model.config
 max_length = config.max_position_embeddings
@@ -132,4 +135,4 @@ print("Sequence Embeddings:", seq_embeddings)
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.07272},
 }
-```

 ---
+library_name: transformers
 license: mit
 pipeline_tag: text-generation
 tags:
 - biology
 - genomics
 - long-context
 ---
 # GENERator-eukaryote-3b-base model
 ## Abouts
 In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 3B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. The extensive and diverse pre-training data endow the GENERator with enhanced understanding and generation capabilities across various organisms.
+For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://huggingface.co/papers/2502.07272).
+Code: https://github.com/GenerTeam/GENERator
 ## How to use
 ### Simple example1:  generation
 # Load the tokenizer and model.
 tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-3b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-eukaryote-3b-base")
 config = model.config
 max_length = config.max_position_embeddings
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.07272},
 }
+```