Instructions to use sudy-super/baku-10b-chat-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sudy-super/baku-10b-chat-v2 with Transformers:

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

pipe = pipeline("text-generation", model="sudy-super/baku-10b-chat-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("sudy-super/baku-10b-chat-v2")
model = AutoModelForMultimodalLM.from_pretrained("sudy-super/baku-10b-chat-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sudy-super/baku-10b-chat-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sudy-super/baku-10b-chat-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sudy-super/baku-10b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sudy-super/baku-10b-chat-v2

SGLang

How to use sudy-super/baku-10b-chat-v2 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 "sudy-super/baku-10b-chat-v2" \
    --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": "sudy-super/baku-10b-chat-v2",
		"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 "sudy-super/baku-10b-chat-v2" \
        --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": "sudy-super/baku-10b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sudy-super/baku-10b-chat-v2 with Docker Model Runner:
```
docker model run hf.co/sudy-super/baku-10b-chat-v2
```

Description

This model is a 10.2 billion parameter model that combines two sets of 24 layers each from CALM2-7B-chat using slerp-merge.

Chat Template

USER: {user_message1}
ASSISTANT: {assistant_message1}<|endoftext|>
USER: {user_message2}
ASSISTANT: {assistant_message2}<|endoftext|>
USER: {user_message3}
ASSISTANT: {assistant_message3}<|endoftext|>

Tutorial

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("sudy-super/baku-10b-chat-v2")
model = AutoModelForCausalLM.from_pretrained("sudy-super/baku-10b-chat-v2", device_map="auto", torch_dtype=torch.bfloat16)

raw_prompt = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
prompt = f"USER:{raw_prompt}\nASSISTANT:"

token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=100,
        do_sample=True,
        temperature=0.8,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

result = tokenizer.decode(output_ids.tolist()[0])
print(result)

Downloads last month: 3