Instructions to use MLGResearch/cleaver_t5g_ss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MLGResearch/cleaver_t5g_ss with Transformers:

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

pipe = pipeline("text-generation", model="MLGResearch/cleaver_t5g_ss")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MLGResearch/cleaver_t5g_ss")
model = AutoModelForSeq2SeqLM.from_pretrained("MLGResearch/cleaver_t5g_ss")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MLGResearch/cleaver_t5g_ss with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MLGResearch/cleaver_t5g_ss"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLGResearch/cleaver_t5g_ss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MLGResearch/cleaver_t5g_ss

SGLang

How to use MLGResearch/cleaver_t5g_ss 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 "MLGResearch/cleaver_t5g_ss" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLGResearch/cleaver_t5g_ss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MLGResearch/cleaver_t5g_ss" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLGResearch/cleaver_t5g_ss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MLGResearch/cleaver_t5g_ss with Docker Model Runner:
```
docker model run hf.co/MLGResearch/cleaver_t5g_ss
```

T5Gemma Fine-tuned Model

This is a fine-tuned T5Gemma model for text-to-text generation tasks.

Model Details

Base Model: google/t5gemma-s-s-ul2-it
Architecture: T5GemmaForConditionalGeneration
Task: Text-to-text generation
Framework: Transformers

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("your-username/model-name")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/model-name")

# Use with chat template
messages = [{"role": "user", "content": "Your input text here"}]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids, max_new_tokens=1024, temperature=0.1, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for MLGResearch/cleaver_t5g_ss

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