Instructions to use CultriX/Qwen2.5-14B-Brocav6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/Qwen2.5-14B-Brocav6 with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/Qwen2.5-14B-Brocav6")
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-Brocav6")
model = AutoModelForCausalLM.from_pretrained("CultriX/Qwen2.5-14B-Brocav6")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CultriX/Qwen2.5-14B-Brocav6 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-Brocav6"
# 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-Brocav6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/Qwen2.5-14B-Brocav6

SGLang

How to use CultriX/Qwen2.5-14B-Brocav6 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-Brocav6" \
    --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-Brocav6",
		"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-Brocav6" \
        --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-Brocav6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/Qwen2.5-14B-Brocav6 with Docker Model Runner:
```
docker model run hf.co/CultriX/Qwen2.5-14B-Brocav6
```
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 della_linear merge method using CultriX/Qwen2.5-14B-Wernickev3 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: della_linear
base_model: CultriX/Qwen2.5-14B-Wernickev3
dtype: bfloat16
parameters:
  epsilon: 0.01  # Reduced from 0.012 for even finer parameter scaling, enhancing precision in blending.
  lambda: 1.5    # Increased from 1.4 to further emphasize significant model contributions, particularly from specialized models.
  normalize: true # Maintains balanced parameter integration, crucial for stability across diverse benchmarks.

adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.65         # Increased from 1.6 to further boost logical reasoning, leveraging Qwen2.5-14B-Broca's strength.
    tinyHellaswag: 1.55   # Slightly increased from 1.5 to enhance contextual understanding, supported by SeQwence-14Bv1.
    tinyMMLU: 1.7         # Increased from 1.65 for improved domain knowledge, utilizing Qwenfinity-2.5-14B's broad capabilities.
    tinyTruthfulQA: 1.95  # Slightly increased from 1.9 to maximize accurate factual reasoning, with Qwenfinity-2.5-14B's contribution.
    tinyTruthfulQA_mc1: 1.75 # Increased from 1.7 for enhanced multiple-choice reasoning, supported by Qwen2.5-14B-Emergedv3.
    tinyWinogrande: 1.8   # Increased from 1.75 for advanced reasoning and contextual prediction, leveraging Qwen2.5-14B-Broca.
    IFEval: 2.0            # Increased from 1.9 to prioritize instruction-following, with Q2.5-Veltha-14B-0.5's strong performance.
    BBH: 1.75              # Slightly increased from 1.7 for complex reasoning, supported by SeQwence-14B-EvolMerge's strength.
    MATH: 2.2              # Increased from 2.1 to maximize mathematical reasoning, with Qwen2.5-Math-14B-Instruct's specialization.
    GPQA: 1.85             # Increased from 1.8 for enhanced graduate-level QA, leveraging Qwen2.5-14B-Wernicke's capabilities.
    MUSR: 1.95             # Increased from 1.9 for strengthened multi-step reasoning, with Qwen2.5-14B-Vimarckoso's expertise.
    MMLU-PRO: 1.85         # Increased from 1.8 to further boost domain multitask performance, utilizing QwenSlerp6-14B.
  smoothing_factor: 0.08 # Reduced from 0.1 for more precise blending, allowing distinct model strengths to be preserved.

gradient_clipping:
  CultriX/Qwen2.5-14B-Wernickev3: 0.85  # Slightly reduced from 0.86 to allow a bit more contribution from the base model.
  CultriX/Qwenfinity-2.5-14B: 0.82      # Reduced from 0.83 to balance its broad multitask contribution.
  djuna/Q2.5-Veltha-14B-0.5: 0.92       # Slightly increased from 0.91 to allow more contribution in advanced reasoning.
  CultriX/Qwen2.5-14B-Broca: 0.86       # Slightly increased from 0.85 to leverage its logical reasoning strengths.
  qingy2019/Qwen2.5-Math-14B-Instruct: 0.94 # Increased from 0.93 to maximize its mathematical reasoning contribution.
  CultriX/SeQwence-14Bv1: 0.87          # Slightly reduced from 0.88 to balance its generalist multitask support.
  sometimesanotion/Qwen2.5-14B-Vimarckoso: 0.90 # Increased from 0.89 for enhanced multi-step reasoning.
  allknowingroger/QwenSlerp6-14B: 0.86  # Slightly reduced from 0.87 to refine its contextual reasoning integration.

models:
  - model: CultriX/Qwen2.5-14B-Wernickev3
    parameters:
      weight: 0.25       # Slightly reduced from 0.26 to balance with other models while maintaining a strong foundation.
      density: 0.72      # Increased from 0.7 to preserve more of its critical reasoning parameters.
  - model: CultriX/Qwenfinity-2.5-14B
    parameters:
      weight: 0.22       # Slightly reduced from 0.23 for a more balanced contribution across its broad capabilities.
      density: 0.68      # Increased from 0.65 to retain more of its multitask performance.
  - model: djuna/Q2.5-Veltha-14B-0.5
    parameters:
      weight: 0.20       # Reduced from 0.22 to balance its specialized contributions with the overall blend.
      density: 0.75      # Increased from 0.72 to further leverage its strengths in IFEval and advanced reasoning.
  - model: CultriX/Qwen2.5-14B-Broca
    parameters:
      weight: 0.16       # Slightly increased from 0.15 to enhance its logical reasoning and factual QA contributions.
      density: 0.68      # Increased from 0.65 to preserve more of its capabilities in the tiny benchmarks.
  - model: qingy2019/Qwen2.5-Math-14B-Instruct
    parameters:
      weight: 0.19       # Slightly increased from 0.18 to further emphasize mathematical reasoning.
      density: 0.75      # Increased from 0.73 to retain more of its specialized mathematical parameters.
  - model: CultriX/SeQwence-14Bv1
    parameters:
      weight: 0.13       # Slightly reduced from 0.14 to fine-tune its generalist multitask support.
      density: 0.65      # Increased from 0.63 to preserve more of its diverse capabilities.
  - model: sometimesanotion/Qwen2.5-14B-Vimarckoso
    parameters:
      weight: 0.11       # Slightly reduced from 0.12 to balance its multi-step reasoning contributions.
      density: 0.62      # Increased from 0.6 to retain more of its specialized reasoning strengths.
  - model: allknowingroger/QwenSlerp6-14B
    parameters:
      weight: 0.09       # Slightly reduced from 0.1 to refine its contextual reasoning contributions.
      density: 0.65      # Increased from 0.62 to preserve more of its capabilities in MMLU-PRO and contextual tasks.