Instructions to use CultriX/Qwen2.5-14B-Qwentangledv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/Qwen2.5-14B-Qwentangledv2 with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/Qwen2.5-14B-Qwentangledv2")
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-Qwentangledv2")
model = AutoModelForCausalLM.from_pretrained("CultriX/Qwen2.5-14B-Qwentangledv2")
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-Qwentangledv2 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-Qwentangledv2"
# 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-Qwentangledv2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/Qwen2.5-14B-Qwentangledv2

SGLang

How to use CultriX/Qwen2.5-14B-Qwentangledv2 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-Qwentangledv2" \
    --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-Qwentangledv2",
		"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-Qwentangledv2" \
        --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-Qwentangledv2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/Qwen2.5-14B-Qwentangledv2 with Docker Model Runner:
```
docker model run hf.co/CultriX/Qwen2.5-14B-Qwentangledv2
```
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 Linear DELLA merge method using CultriX/Enhanced-TIES-Base-v1 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: SuperMerge-LayeredTIES-v1
merge_method: della_linear
base_model: CultriX/Enhanced-TIES-Base-v1 # Referencing the TIES base model defined below (now inlined)
tokenizer_source: base
dtype: float32
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
  t: [0.1, 0.3, 0.7, 0.7, 0.4, 0.2]

slices:
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [0, 8]
        parameters:
          weight: 0.7
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [0, 8]
        parameters:
          weight: 0.3
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [0, 8]
        parameters:
          weight: 0.0
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [0, 8]
        parameters:
          weight: 0.0
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [8, 16]
        parameters:
          weight: 0.4
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [8, 16]
        parameters:
          weight: 0.3
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [8, 16]
        parameters:
          weight: 0.3
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [8, 16]
        parameters:
          weight: 0.0
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [16, 24]
        parameters:
          weight: 0.2
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [16, 24]
        parameters:
          weight: 0.2
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [16, 24]
        parameters:
          weight: 0.5
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [16, 24]
        parameters:
          weight: 0.1
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [24, 32]
        parameters:
          weight: 0.25
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [24, 32]
        parameters:
          weight: 0.1
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [24, 32]
        parameters:
          weight: 0.4
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [24, 32]
        parameters:
          weight: 0.25
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [32, 40]
        parameters:
          weight: 0.4
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [32, 40]
        parameters:
          weight: 0.0
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [32, 40]
        parameters:
          weight: 0.2
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [32, 40]
        parameters:
          weight: 0.4
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [40, 48]
        parameters:
          weight: 0.6
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [40, 48]
        parameters:
          weight: 0.0
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [40, 48]
        parameters:
          weight: 0.1
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [40, 48]
        parameters:
          weight: 0.3



# Commentary:
# =============================================================================
# SuperMerge-LayeredTIES-v1 Commentary:
#
# This configuration combines the strengths of both Enhanced-LayeredSlerp-v1 and SuperMerge-Enhanced-v1.
# It leverages the robust foundation of a TIES-merged base model (Enhanced-TIES-Base-v1) and applies
# the layer-wise module approach and fine-grained weight control from SuperMerge-Enhanced-v1 in a SLERP merge.
#
# Key Features:
#   - TIES-Merged Base Foundation:  Uses 'Enhanced-TIES-Base-v1' as the base model for the SLERP merge.
#     This TIES base provides a selectively merged and potentially more efficient starting point, incorporating
#     strengths from multiple models (Virtuoso, Phi-4, Qwenvergence, DeepSeek) with density control.
#
#   - Layer-wise Module Integration in SLERP:  Maintains the module-based slice structure from SuperMerge-Enhanced-v1.
#     The SLERP merge now combines the TIES-merged base with specialized modules for Reasoning, IFEval, and MATH/Knowledge
#     at different layer ranges, using explicit weights for fine-grained control.
#
#   - Benchmark-Driven Iterative Weight Tuning:  The configuration is designed to be optimized through a
#     benchmark-driven iterative weight tuning process (as described in the refined SuperMerge-Enhanced-v1 approach).
#     The initial weights provided are starting points and need to be systematically tuned based on benchmark results.
#
# Tuning Process (Same as Refined SuperMerge-Enhanced-v1):
#   1. Initial Benchmarking: Run a full benchmark suite.
#   2. Performance Analysis: Examine per-benchmark scores and compare to source models.
#   3. Targeted Weight Adjustments: Adjust layer weights based on performance analysis (e.g., increase IFEval module weight
#      in early layers if IFEval is weak).
#   4. Iterate: Repeat steps 1-3. Make small, incremental adjustments in each iteration.
#
# Rationale:
#   - By using a TIES-merged base, we aim to create a more robust and potentially efficient foundation for the SLERP merge.
#   - The layer-wise module approach and fine-grained weights in SLERP still allow for precise control over the blending
#     of specialized capabilities at different network depths, building upon the solid TIES base.
#   - The emphasis on a benchmark-driven iterative weight tuning process remains crucial for achieving optimal performance.
#
# Next Steps:
#   - Implement this configuration using MergeKit.
#   - Run initial benchmarks to establish a baseline.
#   - Begin the iterative benchmark-driven weight tuning process to optimize performance.
# =============================================================================