Instructions to use OrobasVault/Geodesic-Phantom-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrobasVault/Geodesic-Phantom-12B with Transformers:

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

pipe = pipeline("text-generation", model="OrobasVault/Geodesic-Phantom-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OrobasVault/Geodesic-Phantom-12B")
model = AutoModelForCausalLM.from_pretrained("OrobasVault/Geodesic-Phantom-12B")
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]:]))

NeMo
How to use OrobasVault/Geodesic-Phantom-12B with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OrobasVault/Geodesic-Phantom-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrobasVault/Geodesic-Phantom-12B"
# 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/Geodesic-Phantom-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrobasVault/Geodesic-Phantom-12B

SGLang

How to use OrobasVault/Geodesic-Phantom-12B 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/Geodesic-Phantom-12B" \
    --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/Geodesic-Phantom-12B",
		"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/Geodesic-Phantom-12B" \
        --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/Geodesic-Phantom-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OrobasVault/Geodesic-Phantom-12B with Docker Model Runner:
```
docker model run hf.co/OrobasVault/Geodesic-Phantom-12B
```

Geodesic-Phantom-12B / README.md

OrobasVault

Update README.md

cd6cc5e verified 7 days ago

preview code

raw

history blame contribute delete

4.88 kB

	---
	base_model:
	- mistralai/Mistral-Nemo-Instruct-2407
	- Vortex5/Prototype-X-12b
	- Vortex5/Stellar-Witch-12B
	- Vortex5/Celestial-Queen-12B
	- Vortex5/Moonlit-Mirage-12B
	- Vortex5/Crimson-Constellation-12B
	- Vortex5/Wicked-Nebula-12B
	library_name: transformers
	tags:
	- mergekit
	- merge
	- mistral
	- nemo
	- karcher_stock
	widget:
	- text: "Geodesic-Phantom-12B"
	output:
	url: https://cdn-uploads.huggingface.co/production/uploads/69e46bb84df2a2575b60a527/7tnIXKdUUtGLGkbcGPRGK.jpeg
	---
	# 👻 Geodesic Phantom 12B

	![geodesic-phantom](https://cdn-uploads.huggingface.co/production/uploads/69e46bb84df2a2575b60a527/7tnIXKdUUtGLGkbcGPRGK.jpeg)

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	This was merged in 7 hours on a runpod A40 using an [adaptive VRAM chunking script](https://huggingface.co/spaces/Naphula/model_tools/blob/main/graph_v18_runpod_A40.py) (based on `measure.py` by [GrimJim](https://huggingface.co/grimjim))

	```bat
	WARNING:mergekit.graph:OOM at chunk 65536, reducing to 32768 (attempt 1, progress: 0/131075)
	WARNING:mergekit.graph:OOM at chunk 32768, reducing to 16384 (attempt 2, progress: 0/131075)

	[Karcher_Stock Audit] Layer: lm_head.weight
	Stats: Cos(θ): 0.564 \| t-factor: 0.8843 \| Karcher Iters: 2960
	(Base) mistralai--Mistral-Nemo-Instruct-2407 : █████ ( 11.57%)
	(Donor) Vortex5--Prototype-X-12b : ███████ ( 14.74%)
	(Donor) Vortex5--Stellar-Witch-12B : ███████ ( 14.74%)
	(Donor) Vortex5--Celestial-Queen-12B : ███████ ( 14.74%)
	(Donor) Vortex5--Moonlit-Mirage-12B : ███████ ( 14.74%)
	(Donor) Vortex5--Crimson-Constellation-12B : ███████ ( 14.74%)
	(Donor) Vortex5--Wicked-Nebula-12B : ███████ ( 14.74%)
	```

	The following patch was also required for this merge

	# `karcher_stock` Adaptive Tanh Soft-Clamp v11

	```py
	# ── 11. Model Stock t factor with Adaptive Soft-Clamp ─────────────
	N = len(ws_2d)
	ct = cos_theta.unsqueeze(-1) if cos_theta.dim() > 0 else cos_theta

	# Raw Model Stock formula
	denom = 1.0 + (N - 1) * ct
	# Add a tiny epsilon to prevent literal division by zero
	t_raw = (N * ct) / denom.clamp(min=1e-6)

	# --- BULLETPROOF TANH CLAMP ---
	# 1. Prevent negative infinity spikes (fallback to base model)
	t_clamped_bottom = torch.clamp(t_raw, min=0.0)

	# 2. Smoothly asymptote positive spikes to L (Maximum allowed t-factor)
	L = 1.5
	excess = torch.clamp(t_clamped_bottom - 1.0, min=0.0)
	t_soft_top = 1.0 + (L - 1.0) * torch.tanh(excess / (L - 1.0))

	# 3. Apply: If t <= 1.0, use exact math. If t > 1.0, use soft curve.
	t = torch.where(t_clamped_bottom <= 1.0, t_clamped_bottom, t_soft_top)
	# ------------------------------
	```

	## Example of the clamp preventing merge corruption
	![tanh_clamp](https://cdn-uploads.huggingface.co/production/uploads/68e840caa318194c44ec2a04/eRdxOMhKsRysDgP-6Pkw0.png)

	## Merge Details
	### Merge Method

	This model was merged using the `karcher_stock` merge method using /workspace/models/mistralai--Mistral-Nemo-Instruct-2407 as a base.

	### Models Merged

	The following models were included in the merge:
	* /workspace/models/Vortex5--Wicked-Nebula-12B
	* /workspace/models/Vortex5--Celestial-Queen-12B
	* /workspace/models/Vortex5--Moonlit-Mirage-12B
	* /workspace/models/Vortex5--Stellar-Witch-12B
	* /workspace/models/Vortex5--Prototype-X-12b
	* /workspace/models/Vortex5--Crimson-Constellation-12B

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	architecture: MistralForCausalLM
	base_model: /workspace/models/mistralai--Mistral-Nemo-Instruct-2407
	models:
	- model: /workspace/models/Vortex5--Prototype-X-12b
	- model: /workspace/models/Vortex5--Celestial-Queen-12B
	- model: /workspace/models/Vortex5--Wicked-Nebula-12B
	- model: /workspace/models/Vortex5--Stellar-Witch-12B
	- model: /workspace/models/Vortex5--Moonlit-Mirage-12B
	- model: /workspace/models/Vortex5--Crimson-Constellation-12B
	merge_method: karcher_stock # v8
	parameters:
	filter_wise: true
	max_iter: 10000
	min_iter: 1000
	tol: 1.0e-11
	dtype: float32
	out_dtype: bfloat16
	tokenizer:
	source: union
	chat_template: auto
	name: 👻 Geodesic Phantom 12B
	```