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

Libraries

How to use CultriX/SeQwence-14B-EvolMerge with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/CultriX/SeQwence-14B-EvolMerge

SGLang

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

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

final_model

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 CultriX/SeQwence-14Bv1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: CultriX/SeQwence-14Bv1
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 48]
    model: CultriX/SeQwence-14Bv1
    parameters:
      density: [0.9723868064882017, 1.0, 1.0, 1.0, 1.0, 0.9714039829478123]
      weight: [0.303941801676895, 0.364404551023674, 0.315900913803921, 0.3276032249804535,
        0.32167313684876814, 0.4385348686221433]
  - layer_range: [0, 48]
    model: CultriX/Qwestion-14B
    parameters:
      density: [1.0, 0.9914516102369406, 1.0, 0.8035966798672015, 0.8192028457518323,
        0.9514479609471497]
      weight: [0.23754044230348376, 0.26302919982461254, 0.26313082788173275, 0.17815237275761467,
        0.34301750695974753, 0.5374787613924082]
  - layer_range: [0, 48]
    model: CultriX/Qwen2.5-14B-Wernicke
    parameters:
      density: [0.9250003667144193, 0.9603820599250329, 0.8766642760655986, 1.0, 0.9993615706551808,
        0.7459506348277176]
      weight: [0.48038202535582214, 0.5870170049221364, 0.27054455623315504, 0.06016442415521043,
        0.4012739361231067, 0.26890177448533076]