Instructions to use karakuri-ai/karakuri-lm-32b-thinking-2501-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use karakuri-ai/karakuri-lm-32b-thinking-2501-exp with Transformers:

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

pipe = pipeline("text-generation", model="karakuri-ai/karakuri-lm-32b-thinking-2501-exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("karakuri-ai/karakuri-lm-32b-thinking-2501-exp")
model = AutoModelForMultimodalLM.from_pretrained("karakuri-ai/karakuri-lm-32b-thinking-2501-exp")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use karakuri-ai/karakuri-lm-32b-thinking-2501-exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "karakuri-ai/karakuri-lm-32b-thinking-2501-exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "karakuri-ai/karakuri-lm-32b-thinking-2501-exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/karakuri-ai/karakuri-lm-32b-thinking-2501-exp

SGLang

How to use karakuri-ai/karakuri-lm-32b-thinking-2501-exp 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 "karakuri-ai/karakuri-lm-32b-thinking-2501-exp" \
    --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": "karakuri-ai/karakuri-lm-32b-thinking-2501-exp",
		"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 "karakuri-ai/karakuri-lm-32b-thinking-2501-exp" \
        --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": "karakuri-ai/karakuri-lm-32b-thinking-2501-exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use karakuri-ai/karakuri-lm-32b-thinking-2501-exp with Docker Model Runner:
```
docker model run hf.co/karakuri-ai/karakuri-lm-32b-thinking-2501-exp
```

ynakashima commited on Jan 23, 2025

Commit

e77c18c

verified ·

1 Parent(s): 43499ad

Update README.md

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@@ -1,3 +1,74 @@
----
-license: apache-2.0
----

+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+- ja
+base_model: Qwen/QwQ-32B-Preview
+---
+# KARAKURI LM 32B Thinking 2501 Experimental
+## Model Details
+### Model Description
+- **Developed by:** [KARAKURI Inc.](https://about.karakuri.ai/)
+- **Model type:** Causal Language Models
+- **Languages**: Japanese
+- **License:** Apache 2.0
+- **Finetuned from model:** [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)
+- **Contact**: For questions and comments about the model, please email `karakuri-rd@karakuri.ai`
+- **Demo**: https://lm.karakuri.cc/
+## Usage
+### Run the model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "karakuri-ai/karakuri-lm-32b-thinking-2501-exp"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+messages = [
+    {"role": "user", "content": "こんにちは。"}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+outputs = model.generate(input_ids, max_new_tokens=512)
+tokenizer.decode(outputs[0][input_ids.shape[-1]:])
+```
+## Training Details
+### Training Infrastructure
+- **Hardware**: The model was trained on 16 nodes of an Amazon EC2 trn1.32xlarge instance.
+- **Software**: We use code based on  [neuronx-nemo-megatron](https://github.com/aws-neuron/neuronx-nemo-megatron).
+## Acknowledgments
+This work was supported by the Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO) through the [Generative AI Accelerator Challenge (GENIAC)](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html).
+## Citation
+```
+@misc{karakuri_lm_32b_thinking_2501_exp,
+	author       = { {KARAKURI} {I}nc. },
+	title        = { {KARAKURI} {LM} 32{B} {T}hinking 2501 {E}xperimental },
+	year         = { 2025 },
+	url          = { https://huggingface.co/karakuri-ai/karakuri-lm-32b-thinking-2501-exp },
+	publisher    = { Hugging Face },
+    journal      = { Hugging Face repository }
+}
+```