Instructions to use CultriX/Qwen2.5-DeepHyper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/Qwen2.5-DeepHyper with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/Qwen2.5-DeepHyper")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CultriX/Qwen2.5-DeepHyper")
model = AutoModelForCausalLM.from_pretrained("CultriX/Qwen2.5-DeepHyper")
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

vLLM

How to use CultriX/Qwen2.5-DeepHyper with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CultriX/Qwen2.5-DeepHyper"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CultriX/Qwen2.5-DeepHyper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/Qwen2.5-DeepHyper

SGLang

How to use CultriX/Qwen2.5-DeepHyper 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 "CultriX/Qwen2.5-DeepHyper" \
    --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": "CultriX/Qwen2.5-DeepHyper",
		"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 "CultriX/Qwen2.5-DeepHyper" \
        --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": "CultriX/Qwen2.5-DeepHyper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/Qwen2.5-DeepHyper with Docker Model Runner:
```
docker model run hf.co/CultriX/Qwen2.5-DeepHyper
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-DeepHyper

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

Qwen/Qwen2.5-14B-Instruct
CultriX/Qwen2.5-14B-Hyperionv3_r128
/root/.cache/huggingface/hub/models--CultriX--Qwen2.5-14B-DeepSeek_r128/snapshots/1bca847f92fced165076d9ac921a1e3ef01fcd7f/
CultriX/Qwen2.5-14B_Virtuoso-small-v2-LoRA_r128

Configuration

The following YAML configuration was used to produce this model:

base_model: Qwen/Qwen2.5-14B
models:
  # Each adapter was extracted (rank=128) from its respective finetuned model.
  # Their weights are set lower than the full instruct model (which is now the base)
  - model: CultriX/Qwen2.5-14B-Hyperionv3_r128
    parameters:
      weight: 0.9  # Reduced weight relative to base
      density: 0.9

  - model: CultriX/Qwen2.5-14B_Virtuoso-small-v2-LoRA_r128
    parameters:
      weight: 1.0
      density: 1.0

  - model: Qwen/Qwen2.5-14B-Instruct
    parameters:
      weight: 0.75
      density: 0.75

  - model: /root/.cache/huggingface/hub/models--CultriX--Qwen2.5-14B-DeepSeek_r128/snapshots/1bca847f92fced165076d9ac921a1e3ef01fcd7f/
    parameters:
      weight: 1.00
      density: 1.00

# Merging method and overall parameters
merge_method: dare_ties         # Ties corresponding weights across sources.
parameters:
  weight: 1.0                 # Overall scaling factor.
  density: 1.0                # Overall density (typically left at 1.0).
  normalize: true             # Normalize each set of weights before merging.
  int8_mask: true             # Enable masking if using int8 quantized weights.

# Use the instruct tokenizer to ensure compatibility.
tokenizer_source: CultriX/Qwen2.5-14B_Virtuoso-small-v2-LoRA_r128

# Data type for merged weights.
dtype: bfloat16