Instructions to use CultriX/SeQwence-14Bv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/SeQwence-14Bv1 with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/SeQwence-14Bv1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CultriX/SeQwence-14Bv1")
model = AutoModelForCausalLM.from_pretrained("CultriX/SeQwence-14Bv1")
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 CultriX/SeQwence-14Bv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CultriX/SeQwence-14Bv1"
# 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/SeQwence-14Bv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/SeQwence-14Bv1

SGLang

How to use CultriX/SeQwence-14Bv1 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/SeQwence-14Bv1" \
    --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/SeQwence-14Bv1",
		"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/SeQwence-14Bv1" \
        --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/SeQwence-14Bv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/SeQwence-14Bv1 with Docker Model Runner:
```
docker model run hf.co/CultriX/SeQwence-14Bv1
```
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 Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


models:
  - model: CultriX/Qwen2.5-14B-Wernicke
    parameters:
      weight: 0.35      # Strong performance in GPQA, MUSR, and MMLU-PRO
      density: 0.6      # Retain 60% of significant parameters
  - model: VAGOsolutions/SauerkrautLM-v2-14b-DPO
    parameters:
      weight: 0.30      # Exceptional IFEval and MATH Level 5 capabilities
      density: 0.6      # Retain 60% of significant parameters
  - model: CultriX/Qwen2.5-14B-MegaMerge-pt2
    parameters:
      weight: 0.20      # Balanced contributions to Truthful QA and MMLU
      density: 0.5      # Retain 50% of significant parameters
  - model: CultriX/SeQwence-14B
    parameters:
      weight: 0.15      # Provides diverse data and generalization
      density: 0.4      # Retain 40% of significant parameters
  - model: v000000/Qwen2.5-Lumen-14B
    parameters:
      weight: 0.10      # Enhances creative and narrative tasks
      density: 0.5      # Retain 50% for task diversity
base_model: Qwen/Qwen2.5-14B
merge_method: dare_ties
parameters:
  normalize: true       # Ensures parameter scaling compatibility
  int8_mask: true       # Optimizes memory and computational efficiency
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct