Instructions to use r1char9/T5_chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r1char9/T5_chat with Transformers:

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

pipe = pipeline("text-generation", model="r1char9/T5_chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("r1char9/T5_chat")
model = AutoModelForSeq2SeqLM.from_pretrained("r1char9/T5_chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r1char9/T5_chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r1char9/T5_chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r1char9/T5_chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/r1char9/T5_chat

SGLang

How to use r1char9/T5_chat 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 "r1char9/T5_chat" \
    --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": "r1char9/T5_chat",
		"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 "r1char9/T5_chat" \
        --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": "r1char9/T5_chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use r1char9/T5_chat with Docker Model Runner:
```
docker model run hf.co/r1char9/T5_chat
```

T5_chat

A fine-tuned version of ai-forever/ruT5-base for open-domain text generation / conversational response generation in Russian.

The model takes a text prompt (e.g. a question or a conversational turn) and generates a free-form text continuation or reply.

Usage

from transformers import AutoTokenizer, T5ForConditionalGeneration

chat_checkpoint = "r1char9/T5_chat"
chat_model = T5ForConditionalGeneration.from_pretrained(chat_checkpoint)
chat_tokenizer = AutoTokenizer.from_pretrained(chat_checkpoint)


def chat_fun(text: str):
    tokenized_sentence = chat_tokenizer(text, return_tensors="pt", truncation=True)
    output = chat_model.generate(**tokenized_sentence, num_beams=2, max_length=100)
    return chat_tokenizer.decode(output[0], skip_special_tokens=True)


text = "Что самое главное в человеке ?"
response = chat_fun(text)

print(response)
# Самое главное в человеке - это его любовь и уважение к другим людям.
# Это означает, что он должен быть искренним и искренним в своих мыслях и чувствах,
# а также готов жертвовать своим личным и профессиональным идеалами и ценностям, чтобы достичь своих целей.

Limitations

The model may produce factually inaccurate, repetitive, or inconsistent responses, as is common for smaller open-domain generation models.
No content filtering or safety alignment has been applied — outputs should be reviewed before use in any user-facing application.

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Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for r1char9/T5_chat

Base model

ai-forever/ruT5-base

Finetuned

(24)

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Collection including r1char9/T5_chat

Seq2Seq Models

Collection

4 items • Updated 7 days ago