Instructions to use OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1")
model = AutoModelForCausalLM.from_pretrained("OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1")
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 OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1

SGLang

How to use OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1 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 "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1" \
    --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": "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1",
		"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 "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1" \
        --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": "OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1 with Docker Model Runner:
```
docker model run hf.co/OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1
```

⚠️ Warning: This merge produces BROKEN output and is not recommended to download. The tensorguard method needs revision.

💂 TensorGuard-Prototype-24B-v1

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TensorGuard merge method.

Models Merged

The following models were included in the merge:

/workspace/Naphula--BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonly
/workspace/TheDrummer--Magidonia-24B-v4.3
/workspace/TheDrummer--Precog-24B-v1
/workspace/TheDrummer--Cydonia-24B-v4.3

Configuration

The following YAML configuration was used to produce this model:

architecture: MistralForCausalLM
models:
  - model: /workspace/Naphula--BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonly
  ## 2506 ##
  - model: /workspace/TheDrummer--Cydonia-24B-v4.3
  ## 2509 ##
  - model: /workspace/TheDrummer--Precog-24B-v1
  - model: /workspace/TheDrummer--Magidonia-24B-v4.3
merge_method: tensorguard # https://arxiv.org/abs/2506.01631v2
parameters:
  noise_epsilon: 0.01                    # Noise magnitude for perturbations
  num_perturbations: 30                  # Number of perturbation iterations (paper default)
  noise_strategies: "adversarial,structural,low_freq,high_freq,gaussian" # All noise strategies from paper
  similarity_metric: "frobenius"         # Distance metric: frobenius, spectral, euclidean, cosine
  normalize_weights: true                # Normalize weights to sum to 1
  random_seed: 420                       # Seed for reproducible results
  pca_components: 8                      # PCA components for dimensionality reduction
  use_higher_order_stats: true           # Compute skewness and kurtosis (expensive)
  use_spectral_features: true            # Compute spectral norm features (very expensive)
tokenizer:
  source: union
chat_template: auto
dtype: float32
out_dtype: bfloat16
name: 💂 Tensorguard-24B-v1

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Safetensors

Model size

24B params

Tensor type

BF16

Paper for OrobasVault/BROKEN_MERGE_TensorGuard-Prototype-24B-v1

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification

Paper • 2506.01631 • Published Jul 3, 2025 • 1