Instructions to use Edens-Gate/qwq-32b-magnum-v2-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edens-Gate/qwq-32b-magnum-v2-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Edens-Gate/qwq-32b-magnum-v2-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/qwq-32b-magnum-v2-v1")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/qwq-32b-magnum-v2-v1", 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 Edens-Gate/qwq-32b-magnum-v2-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edens-Gate/qwq-32b-magnum-v2-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edens-Gate/qwq-32b-magnum-v2-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edens-Gate/qwq-32b-magnum-v2-v1

SGLang

How to use Edens-Gate/qwq-32b-magnum-v2-v1 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 "Edens-Gate/qwq-32b-magnum-v2-v1" \
    --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": "Edens-Gate/qwq-32b-magnum-v2-v1",
		"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 "Edens-Gate/qwq-32b-magnum-v2-v1" \
        --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": "Edens-Gate/qwq-32b-magnum-v2-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edens-Gate/qwq-32b-magnum-v2-v1 with Docker Model Runner:
```
docker model run hf.co/Edens-Gate/qwq-32b-magnum-v2-v1
```

Delta-Vector commited on Mar 13, 2025

Commit

4ac573a

verified ·

1 Parent(s): c01028f

Model save

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -83,7 +83,7 @@ micro_batch_size: 2
 num_epochs: 2
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
-learning_rate: 4e-5
 max_grad_norm: 1.0
 train_on_inputs: false
@@ -134,7 +134,7 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 4e-05
 - train_batch_size: 2
 - eval_batch_size: 2
 - seed: 42

 num_epochs: 2
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
+learning_rate: 6e-6
 max_grad_norm: 1.0
 train_on_inputs: false
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 6e-06
 - train_batch_size: 2
 - eval_batch_size: 2
 - seed: 42