Instructions to use GenerTeam/GENERator-eukaryote-1.2b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenerTeam/GENERator-eukaryote-1.2b-base with Transformers:

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

pipe = pipeline("text-generation", model="GenerTeam/GENERator-eukaryote-1.2b-base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenerTeam/GENERator-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-eukaryote-1.2b-base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GenerTeam/GENERator-eukaryote-1.2b-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-1.2b-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-1.2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GenerTeam/GENERator-eukaryote-1.2b-base

SGLang

How to use GenerTeam/GENERator-eukaryote-1.2b-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-1.2b-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-1.2b-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-1.2b-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-1.2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GenerTeam/GENERator-eukaryote-1.2b-base with Docker Model Runner:
```
docker model run hf.co/GenerTeam/GENERator-eukaryote-1.2b-base
```

Add Github link to model card

by nielsr HF Staff - opened Apr 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -1,13 +1,14 @@
 ---
 license: mit
 pipeline_tag: text-generation
 tags:
 - biology
 - genomics
 - long-context
-library_name: transformers
 arxiv: 2502.07272
 ---
 # GENERator-eukaryote-1.2b-base model
 ## Important Notice !!!
@@ -18,7 +19,7 @@ In this repository, we present GENERator, a generative genomic foundation model
 Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of enhancer sequences that regulate gene expression during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.
-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
@@ -138,4 +139,4 @@ 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
 arxiv: 2502.07272
 ---
 # GENERator-eukaryote-1.2b-base model
 ## Important Notice !!!
 Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of enhancer sequences that regulate gene expression during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.
+For more technical details, please refer to our paper [**GENERator: A Long-Context Generative Genomic Foundation Model**](https://huggingface.co/GenerTeam). The code and implementation details are available on Github: [https://github.com/GenerTeam/GENERator](https://github.com/GenerTeam/GENERator).
 ## How to use
 ### Simple example1:  generation
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.07272},
 }
+```