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
```

OrobasVault commited on 7 days ago

Commit

3b81dbc

verified ·

1 Parent(s): 4edccb2

Update README.md

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README.md +64 -4

README.md CHANGED Viewed

@@ -1,19 +1,79 @@
 ---
-base_model: []
 library_name: transformers
 tags:
 - mergekit
 - merge
 ---
-# karcher_stock-12B
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## 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

 ---
+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
 ---
+# 👻 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 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