Instructions to use Naphula/Riemannian-Redshift-12B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Riemannian-Redshift-12B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Naphula/Riemannian-Redshift-12B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula/Riemannian-Redshift-12B-v1")
model = AutoModelForCausalLM.from_pretrained("Naphula/Riemannian-Redshift-12B-v1", device_map="auto")
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 Naphula/Riemannian-Redshift-12B-v1 with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Naphula/Riemannian-Redshift-12B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Riemannian-Redshift-12B-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": "Naphula/Riemannian-Redshift-12B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula/Riemannian-Redshift-12B-v1

SGLang

How to use Naphula/Riemannian-Redshift-12B-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 "Naphula/Riemannian-Redshift-12B-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": "Naphula/Riemannian-Redshift-12B-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 "Naphula/Riemannian-Redshift-12B-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": "Naphula/Riemannian-Redshift-12B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Naphula/Riemannian-Redshift-12B-v1 with Docker Model Runner:
```
docker model run hf.co/Naphula/Riemannian-Redshift-12B-v1
```

Riemannian-Redshift-12B-v1 / README.md

Naphula

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a5c2f95 verified 6 months ago

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base_model:
  - Vortex5/Astral-Noctra-12B
  - Vortex5/Azure-Starlight-12B
  - Vortex5/Crimson-Constellation-12B
  - Vortex5/Red-Synthesis-12B
  - Vortex5/Shining-Seraph-12B
  - Vortex5/Starlit-Shadow-12B
  - Vortex5/Vermilion-Sage-12B
  - Vortex5/Scarlet-Seraph-12B
  - Vortex5/Maroon-Sunset-12B
  - Vortex5/Amber-Starlight-12B
language:
  - en
library_name: transformers
license: apache-2.0
tags:
  - karcher
  - merge
  - mergekit
  - mistral
  - nemo
widget:
  - text: Riemannian-Redshift-12B-v1
    output:
      url: >-
        https://cdn-uploads.huggingface.co/production/uploads/68e840caa318194c44ec2a04/-5OsIfrWlUxoJZP4U83uq.png

⚠️ Note: This model requires Mistral Tekken chat template.

🌌 Riemannian Redshift 12B v1

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

Merge Details

Merge Method

This is an experimental karcher merge of several high quality Vortex5 models. I used float32 precision and max_iter: 1000 to ensure the best bits were chosen for the Riemannian center. This merge took 5 hours using graph_v18 as an accelerant with 8GB VRAM.

This model was merged using the Karcher Mean merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: B:/12B/models--Vortex5--Astral-Noctra-12B
  - model: B:/12B/models--Vortex5--Azure-Starlight-12B
  - model: B:/12B/models--Vortex5--Crimson-Constellation-12B
  - model: B:/12B/models--Vortex5--Red-Synthesis-12B
  - model: B:/12B/models--Vortex5--Shining-Seraph-12B
  - model: B:/12B/models--Vortex5--Starlit-Shadow-12B
  - model: B:/12B/models--Vortex5--Vermilion-Sage-12B
  - model: B:/12B/models--Vortex5--Scarlet-Seraph-12B
  - model: B:/12B/models--Vortex5--Maroon-Sunset-12B
  - model: B:/12B/models--Vortex5--Amber-Starlight-12B
merge_method: karcher
parameters:
  max_iter: 1000
  tol: 1.0e-9
dtype: float32
out_dtype: bfloat16
tokenizer:  
  source: union
chat_template: auto
name: 🌌 Riemannian-Redshift-12B-v1