Instructions to use CultriX/Qwen2.5-14B-DeepResearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/Qwen2.5-14B-DeepResearch with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/Qwen2.5-14B-DeepResearch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CultriX/Qwen2.5-14B-DeepResearch")
model = AutoModelForCausalLM.from_pretrained("CultriX/Qwen2.5-14B-DeepResearch")
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/Qwen2.5-14B-DeepResearch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CultriX/Qwen2.5-14B-DeepResearch"
# 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/Qwen2.5-14B-DeepResearch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/Qwen2.5-14B-DeepResearch

SGLang

How to use CultriX/Qwen2.5-14B-DeepResearch 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/Qwen2.5-14B-DeepResearch" \
    --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/Qwen2.5-14B-DeepResearch",
		"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/Qwen2.5-14B-DeepResearch" \
        --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/Qwen2.5-14B-DeepResearch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/Qwen2.5-14B-DeepResearch with Docker Model Runner:
```
docker model run hf.co/CultriX/Qwen2.5-14B-DeepResearch
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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 suayptalha/Lamarckvergence-14B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

name: OptimalMerge-14B
merge_method: dare_ties
base_model: suayptalha/Lamarckvergence-14B
tokenizer_source: base
dtype: bfloat16
out_dtype: bfloat16

parameters:
  normalize: true
  int8_mask: true

models:
  - model: suayptalha/Lamarckvergence-14B
    parameters:
      weight: 0.35
      density: 0.75
  - model: sthenno/tempesthenno-ppo-ckpt40
    parameters:
      weight: 0.25
      density: 0.7
  - model: tanliboy/lambda-qwen2.5-14b-dpo-test
    parameters:
      weight: 0.2
      density: 0.65
  - model: djuna/Q2.5-Veltha-14B
    parameters:
      weight: 0.1
      density: 0.6
  - model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4
    parameters:
      weight: 0.1
      density: 0.6

adaptive_merge_parameters:
  task_weights:
    IFEval: 1.8
    BBH: 1.5
    MATH: 2.0
    GPQA: 1.4
    MUSR: 1.3
    MMLU-PRO: 1.5
  smoothing_factor: 0.1

gradient_clipping:
  suayptalha/Lamarckvergence-14B: 0.85
  sthenno/tempesthenno-ppo-ckpt40: 0.88
  tanliboy/lambda-qwen2.5-14b-dpo-test: 0.87
  djuna/Q2.5-Veltha-14B: 0.89
  Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4: 0.86