Instructions to use GenerTeam/GENERator-v2-eukaryote-1.2b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenerTeam/GENERator-v2-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-v2-eukaryote-1.2b-base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenerTeam/GENERator-v2-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-v2-eukaryote-1.2b-base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GenerTeam/GENERator-v2-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-v2-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-v2-eukaryote-1.2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GenerTeam/GENERator-v2-eukaryote-1.2b-base

SGLang

How to use GenerTeam/GENERator-v2-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-v2-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-v2-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-v2-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-v2-eukaryote-1.2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GenerTeam/GENERator-v2-eukaryote-1.2b-base with Docker Model Runner:
```
docker model run hf.co/GenerTeam/GENERator-v2-eukaryote-1.2b-base
```

GenerTeam commited on 24 days ago

Commit

da5fa93

verified ·

1 Parent(s): 1d0509f

Update tokenizer.py

Browse files

Files changed (1) hide show

tokenizer.py +4 -4

tokenizer.py CHANGED Viewed

@@ -53,10 +53,10 @@ class DNAKmerTokenizer(PreTrainedTokenizer):
             "|".join(re.escape(token) for token in self.special_tokens)
         )
         self.dna_pattern = re.compile(f"[A-Z]{{{self.k}}}|[A-Z]+")
-        self.bos_token = "<s>"
-        self.eos_token = "</s>"
-        self.bos_token_id = self._convert_token_to_id(self.bos_token)
-        self.eos_token_id = self._convert_token_to_id(self.eos_token)
         super().__init__(**kwargs)
     @property

             "|".join(re.escape(token) for token in self.special_tokens)
         )
         self.dna_pattern = re.compile(f"[A-Z]{{{self.k}}}|[A-Z]+")
+        kwargs.setdefault("bos_token", "<s>")
+        kwargs.setdefault("eos_token", "</s>")
+        kwargs.setdefault("unk_token", "<oov>")
+        kwargs.setdefault("pad_token", "<pad>")
         super().__init__(**kwargs)
     @property