Text Generation
Transformers
Safetensors
English
gemma4
image-text-to-text
31B
roleplay
instruct
creative
mergekit
Merge
karcher_stock
conversational
Instructions to use OrobasVault/Vespera-Synapse-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OrobasVault/Vespera-Synapse-31B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OrobasVault/Vespera-Synapse-31B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("OrobasVault/Vespera-Synapse-31B") model = AutoModelForMultimodalLM.from_pretrained("OrobasVault/Vespera-Synapse-31B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OrobasVault/Vespera-Synapse-31B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OrobasVault/Vespera-Synapse-31B" # 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/Vespera-Synapse-31B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OrobasVault/Vespera-Synapse-31B
- SGLang
How to use OrobasVault/Vespera-Synapse-31B 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/Vespera-Synapse-31B" \ --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/Vespera-Synapse-31B", "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/Vespera-Synapse-31B" \ --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/Vespera-Synapse-31B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OrobasVault/Vespera-Synapse-31B with Docker Model Runner:
docker model run hf.co/OrobasVault/Vespera-Synapse-31B
🌃 Vespera Synapse 31B
This is a merge of pre-trained language models created using mergekit.
The following patch was required for this merge
karcher_stock Adaptive Tanh Soft-Clamp v11
# ── 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)
# ------------------------------
Merge Audit
Merge Details
Merge Method
This model was merged using the karcher_stock merge method using google/gemma-4-31B as a base.
Models Merged
The following models were included in the merge:
- google/gemma-4-31B
- Vortex5/Glimmering-Citrus-31B
- Vortex5/Scarlet-Shadow-31B
- Cyclone-Labs/Twisted-Cyclone-31B
Configuration
The following YAML configuration was used to produce this model:
architecture: Gemma4ForConditionalGeneration
base_model: /workspace/models/google--gemma-4-31B # densenet--Gemma-4-31B-StyleTune-heretic-ara
models:
- model: /workspace/models/Vortex5--Glimmering-Citrus-31B
- model: /workspace/models/Vortex5--Scarlet-Shadow-31B
- model: /workspace/models/Cyclone-Labs--Twisted-Cyclone-31B
merge_method: karcher_stock # v37
parameters:
filter_wise: true
max_iter: 1000
min_iter: 100
tol: 1.0e-9
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
chat_template: auto
name: 🌃 Vespera Synapse 31B
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