Instructions to use rinna/qwq-bakeneko-32b-gptq-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rinna/qwq-bakeneko-32b-gptq-int8 with Transformers:

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

pipe = pipeline("text-generation", model="rinna/qwq-bakeneko-32b-gptq-int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/qwq-bakeneko-32b-gptq-int8")
model = AutoModelForCausalLM.from_pretrained("rinna/qwq-bakeneko-32b-gptq-int8", device_map="auto")
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 Settings

vLLM

How to use rinna/qwq-bakeneko-32b-gptq-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rinna/qwq-bakeneko-32b-gptq-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rinna/qwq-bakeneko-32b-gptq-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rinna/qwq-bakeneko-32b-gptq-int8

SGLang

How to use rinna/qwq-bakeneko-32b-gptq-int8 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 "rinna/qwq-bakeneko-32b-gptq-int8" \
    --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": "rinna/qwq-bakeneko-32b-gptq-int8",
		"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 "rinna/qwq-bakeneko-32b-gptq-int8" \
        --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": "rinna/qwq-bakeneko-32b-gptq-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rinna/qwq-bakeneko-32b-gptq-int8 with Docker Model Runner:
```
docker model run hf.co/rinna/qwq-bakeneko-32b-gptq-int8
```

keisawada commited on Mar 23, 2025

Commit

d76c15f

verified ·

1 Parent(s): b86f66e

Update README.md

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README.md +3 -2

README.md CHANGED Viewed

@@ -3,7 +3,6 @@ thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rin
 license: apache-2.0
 language:
 - ja
-- en
 tags:
 - qwen2
 - conversational
@@ -28,6 +27,7 @@ This model is an 8-bit quantized model for [rinna/qwq-bakeneko-32b](https://hugg
 | Instruction-Tuning Model | Qwen2.5 Bakeneko 32B Instruct [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int4)
 | DeepSeek R1 Distill Qwen2.5 Merged Reasoning Model | DeepSeek R1 Distill Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int4)
 | QwQ Merged Reasoning Model | QwQ Bakeneko 32B [[HF]](https://huggingface.co/rinna/qwq-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/qwq-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/qwq-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int4)
 See [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) for details about model architecture and data.
@@ -50,12 +50,13 @@ See [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) for
 | [rinna/qwen2.5-bakeneko-32b](https://huggingface.co/rinna/qwen2.5-bakeneko-32b) | 79.18 | - | -
 | [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 78.29 | 8.13 | 7.54
 | [rinna/qwen2.5-bakeneko-32b-instruct](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct) | 79.62 | 8.17 | 7.66
 | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | 73.51 | 7.39 | 6.88
 | [rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b) | 77.43 | 8.58 | 8.19
 | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | 76.12 | 8.58 | 8.25
 | [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) | 78.31 | 8.81 | 8.52
-For detailed benchmarking results, please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
 ---

 license: apache-2.0
 language:
 - ja
 tags:
 - qwen2
 - conversational
 | Instruction-Tuning Model | Qwen2.5 Bakeneko 32B Instruct [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int4)
 | DeepSeek R1 Distill Qwen2.5 Merged Reasoning Model | DeepSeek R1 Distill Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int4)
 | QwQ Merged Reasoning Model | QwQ Bakeneko 32B [[HF]](https://huggingface.co/rinna/qwq-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/qwq-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/qwq-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int4)
+| QwQ Bakeneko Merged Instruction-Tuning Model | Qwen2.5 Bakeneko 32B Instruct V2 [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gptq-int4)
 See [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) for details about model architecture and data.
 | [rinna/qwen2.5-bakeneko-32b](https://huggingface.co/rinna/qwen2.5-bakeneko-32b) | 79.18 | - | -
 | [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 78.29 | 8.13 | 7.54
 | [rinna/qwen2.5-bakeneko-32b-instruct](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct) | 79.62 | 8.17 | 7.66
+| [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2) | 77.92 | 8.86 | 8.53
 | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | 73.51 | 7.39 | 6.88
 | [rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b) | 77.43 | 8.58 | 8.19
 | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | 76.12 | 8.58 | 8.25
 | [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) | 78.31 | 8.81 | 8.52
+For detailed benchmarking results, please refer to [rinna's LM benchmark page (Sheet 20250313)](https://rinnakk.github.io/research/benchmarks/lm/index.html).
 ---