Instructions to use Vortex5/Emerald-Wyvern-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortex5/Emerald-Wyvern-12B with Transformers:

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

pipe = pipeline("text-generation", model="Vortex5/Emerald-Wyvern-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Emerald-Wyvern-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Emerald-Wyvern-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vortex5/Emerald-Wyvern-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Emerald-Wyvern-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": "Vortex5/Emerald-Wyvern-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortex5/Emerald-Wyvern-12B

SGLang

How to use Vortex5/Emerald-Wyvern-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 "Vortex5/Emerald-Wyvern-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": "Vortex5/Emerald-Wyvern-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 "Vortex5/Emerald-Wyvern-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": "Vortex5/Emerald-Wyvern-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vortex5/Emerald-Wyvern-12B with Docker Model Runner:
```
docker model run hf.co/Vortex5/Emerald-Wyvern-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Emerald Wyvern

Overview

Emerald-Wyvern-12B was made with a custom method, it merges — Luminous-Shadow-12B, Velvet-Orchid-12B, and Crimson-Twilight-12B, using Harmony-Bird-12B as a base.

Merge Configuration

Expand YAML Configuration


models:
  - model: Vortex5/Luminous-Shadow-12B
    parameters:
      weight:
        - filter: self_attn
          value: [0.20, 0.35, 0.55, 0.75, 1.00, 0.90, 0.25, 0.25]
        - value: 0.33
  - model: Vortex5/Velvet-Orchid-12B
    parameters:
      weight:
        - filter: mlp
          value: [0.20, 0.30, 0.20, 0.7, 0.65, 0.66, 0.0, 0.30]
        - value: 0.33
  - model: Vortex5/Crimson-Twilight-12B
    parameters:
      weight:
        - filter: self_attn
          value: [0.25, 0.35, 0.45, 0.55, 0.60, 0.65, 0.88, 0.88]
        - filter: mlp
          value: [0.20, 0.30, 0.45, 0.65, 0.80, 0.90, 0.95, 0.85]
        - value: 0.33
merge_method: flowsync
base_model: Vortex5/Harmony-Bird-12B
dtype: bfloat16
parameters:
  strength: 1.0
  balance: 0.5
tokenizer:
  source: Vortex5/Harmony-Bird-12B

Custom Merge Method

About

FlowSync

Concept: A coherence-weighted tensor merge algorithm that aligns donor model deltas relative to a shared base, computes pairwise cosine similarities to measure inter-model agreement, and aggregates them along the highest-consensus direction in parameter space. It reinforces semantically consistent weight shifts while suppressing incoherent or noisy deviations using adaptive temperature scaling, semantic gating (Nyström eigenvector extraction), and MAD-based normalization.

Key Parameters

strength (global): Scales how far the merged tensor moves from the base toward the consensus vector. Higher values emphasize donor influence.
balance (global): Modulates bias between logic-dominant (attention/MLP) and style-dominant (embedding/output) layers.

Credits

Team Mradermacher — Static & imatrix quants
DeathGodlike — EXL3 quants
Original creators and model authors

Downloads last month: 4

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for Vortex5/Emerald-Wyvern-12B

Vortex5/Crimson-Twilight-12B

Vortex5/Harmony-Bird-12B

Vortex5/Luminous-Shadow-12B

Vortex5/Velvet-Orchid-12B

Merge model

this model

Quantizations

5 models