Instructions to use CultriX/Qwen2.5-14B-Brocav3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CultriX/Qwen2.5-14B-Brocav3 with Transformers:

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

pipe = pipeline("text-generation", model="CultriX/Qwen2.5-14B-Brocav3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("CultriX/Qwen2.5-14B-Brocav3")
model = AutoModelForMultimodalLM.from_pretrained("CultriX/Qwen2.5-14B-Brocav3")
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/Qwen2.5-14B-Brocav3 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-Brocav3"
# 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-Brocav3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CultriX/Qwen2.5-14B-Brocav3

SGLang

How to use CultriX/Qwen2.5-14B-Brocav3 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-Brocav3" \
    --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-Brocav3",
		"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-Brocav3" \
        --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-Brocav3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CultriX/Qwen2.5-14B-Brocav3 with Docker Model Runner:
```
docker model run hf.co/CultriX/Qwen2.5-14B-Brocav3
```
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: 0.012        # Further reduced to ensure ultra-fine parameter scaling for precision.
  lambda: 1.4           # Stronger emphasis on significant model contributions.
  normalize: true       # Balances the parameter integration for stability.

adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.6           # Prioritizes logical reasoning improvements.
    tinyHellaswag: 1.5     # Strengthened contextual understanding and consistency.
    tinyMMLU: 1.65         # Enhanced domain knowledge for multitask benchmarks.
    tinyTruthfulQA: 1.9    # Maximized for accurate factual reasoning and QA.
    tinyTruthfulQA_mc1: 1.7 # Balanced focus for multiple-choice reasoning.
    tinyWinogrande: 1.75   # Advanced reasoning and contextual prediction improvement.
    IFEval: 1.9            # Instruction-following tasks boosted by multitask contributors.
    BBH: 1.7               # Complex reasoning is supported by logical base models.
    MATH: 2.1              # Highest priority, focusing on mathematical excellence.
    GPQA: 1.8              # Boosted graduate-level QA capabilities.
    MUSR: 1.9              # Nuanced multi-step reasoning strengthened further.
    MMLU-PRO: 1.8          # Domain multitask performance maximized.
  smoothing_factor: 0.1    # Precisely tuned for smooth task-specific blending.

gradient_clipping:
  CultriX/Qwen2.5-14B-Wernickev3: 0.86  # Backbone stability with slightly reduced clipping.
  CultriX/Qwenfinity-2.5-14B: 0.83      # Consistent multitask integration.
  djuna/Q2.5-Veltha-14B-0.5: 0.91       # Strengthened advanced reasoning contributions.
  CultriX/Qwen2.5-14B-Broca: 0.85       # Logical reasoning enhancements stabilized.
  qingy2019/Qwen2.5-Math-14B-Instruct: 0.93 # Mathematically focused tasks maximized.
  CultriX/SeQwence-14Bv1: 0.88          # Generalist multitask support.
  sometimesanotion/Qwen2.5-14B-Vimarckoso: 0.89 # Balanced multi-step reasoning contributions.
  allknowingroger/QwenSlerp6-14B: 0.87  # Contextual and logical reasoning integration refined.

models:
  - model: CultriX/Qwen2.5-14B-Wernickev3
    parameters:
      weight: 0.26       # Core backbone for multitask reasoning.
      density: 0.7       # Slight increase to preserve critical reasoning parameters.
  - model: CultriX/Qwenfinity-2.5-14B
    parameters:
      weight: 0.23       # Comprehensive multitask performer.
      density: 0.65
  - model: djuna/Q2.5-Veltha-14B-0.5
    parameters:
      weight: 0.22       # Advanced reasoning support for GPQA and MUSR.
      density: 0.72
  - model: CultriX/Qwen2.5-14B-Broca
    parameters:
      weight: 0.15       # Logical reasoning and factual QA enhancements.
      density: 0.65
  - model: qingy2019/Qwen2.5-Math-14B-Instruct
    parameters:
      weight: 0.18       # Mathematical reasoning priority.
      density: 0.73
  - model: CultriX/SeQwence-14Bv1
    parameters:
      weight: 0.14       # Generalist multitask backbone.
      density: 0.63
  - model: sometimesanotion/Qwen2.5-14B-Vimarckoso
    parameters:
      weight: 0.12       # Multi-step reasoning tasks contributor.
      density: 0.6
  - model: allknowingroger/QwenSlerp6-14B
    parameters:
      weight: 0.1        # Contextual reasoning improvements.
      density: 0.62

tokenizer_source: CultriX/Qwen2.5-14B-Wernickev3