Instructions to use maldv/QwentileLambda2.5-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/QwentileLambda2.5-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="maldv/QwentileLambda2.5-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/QwentileLambda2.5-32B-Instruct")
model = AutoModelForCausalLM.from_pretrained("maldv/QwentileLambda2.5-32B-Instruct")
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 maldv/QwentileLambda2.5-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/QwentileLambda2.5-32B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/QwentileLambda2.5-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/QwentileLambda2.5-32B-Instruct

SGLang

How to use maldv/QwentileLambda2.5-32B-Instruct 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 "maldv/QwentileLambda2.5-32B-Instruct" \
    --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": "maldv/QwentileLambda2.5-32B-Instruct",
		"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 "maldv/QwentileLambda2.5-32B-Instruct" \
        --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": "maldv/QwentileLambda2.5-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maldv/QwentileLambda2.5-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/maldv/QwentileLambda2.5-32B-Instruct
```

maldv commited on May 17, 2025

Commit

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@@ -45,6 +45,10 @@ In other words, all of these models get warped and interpolated in signal space,
 The latest in my series of Qwen 2.5 merges. Some really good models have been released recently, so I folded them in with Qwentile as the base. It should exhibit superior thinking skills, and perhaps even some code ability. I was satisfied with QReasoner2.5-32B-Instruct for advanced reasoning, but I suspect this will be an improvement.
 ## Citation
 If you find our work helpful, feel free to give us a cite.

 The latest in my series of Qwen 2.5 merges. Some really good models have been released recently, so I folded them in with Qwentile as the base. It should exhibit superior thinking skills, and perhaps even some code ability. I was satisfied with QReasoner2.5-32B-Instruct for advanced reasoning, but I suspect this will be an improvement.
+### A &lt;think&gt; model?
+No, oddly enough, given it's lineage I thought for sure it would be a thought model, but instead it blends thought with it's creative output almost seamlessly. The combination is pretty powerful in my initial tests.
 ## Citation
 If you find our work helpful, feel free to give us a cite.