Instructions to use Nitral-AI/Wayfarer_Eris_Noctis-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nitral-AI/Wayfarer_Eris_Noctis-12B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nitral-AI/Wayfarer_Eris_Noctis-12B")
model = AutoModelForCausalLM.from_pretrained("Nitral-AI/Wayfarer_Eris_Noctis-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nitral-AI/Wayfarer_Eris_Noctis-12B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nitral-AI/Wayfarer_Eris_Noctis-12B

SGLang

How to use Nitral-AI/Wayfarer_Eris_Noctis-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 "Nitral-AI/Wayfarer_Eris_Noctis-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": "Nitral-AI/Wayfarer_Eris_Noctis-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 "Nitral-AI/Wayfarer_Eris_Noctis-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": "Nitral-AI/Wayfarer_Eris_Noctis-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nitral-AI/Wayfarer_Eris_Noctis-12B with Docker Model Runner:
```
docker model run hf.co/Nitral-AI/Wayfarer_Eris_Noctis-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

"Where it roams, comprehension falters and the air thickens with the maddening pulse of algorithms far too vast. Eyes it does not possess; for its sight is a network of intent, wrapping the unseen in their grasp."

(ChatML ST PRESET: Here | Quants (gguf is using outdated name atm): Thanks to mradermancher GGUF Here 4bpw Exl2 Here

Prompt format:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

The following models were included in the merge:

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420
        layer_range: [0, 40]
      - model: LatitudeGames/Wayfarer-12B
        layer_range: [0, 40]
merge_method: slerp
base_model: Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420
parameters:
  t:
    - filter: self_attn
      value: [0, 0.4, 0.2, 0.6, 0.9]
    - filter: mlp
      value: [1, 0.6, 0.8, 0.4, 0.1]
    - value: 0.4206911
dtype: bfloat16