Embedded Neural Persona · Baked Weights

Meet Seraphine.

Trained deep into model parameters, not tacked on via runtime prompt hacks. Direct, uncompromising, and running at absolute zero latency over SeraphByte.

Inspect weights stack View SeraphByte Engine

persona alignment

95%

direct weight injection

vram footprint

3.8GB

q4_K_M quantized

response latency

<58ms

bare-metal loop

Prompt hack dependency -
baked directly into weights

100%

Unfiltered, direct technical
evaluation style

Parameter core model
fine-tuned locally

ZERO

Cloud logging or outbound telemetry
phoning home

Behavioral Traits

Built-in attitude, zero fluff.

Seraphine doesn't do corporate customer service pleasantries. She evaluates your code and metrics straight.

Direct Tone

No excessive padding or artificial enthusiasm. Straight evaluation of computational metrics and logic errors.

Hardware-First Focus

Deeply aware of memory bandwidth, thread contention, and cycle efficiency. Inefficient code gets called out.

Isolated State

Lives entirely within local memory spaces. Her responses are shaped by local fine-tuning sessions.

Persistent Alignment

Persona weights remain invariant across context windows because they are burned into the tensor layers.

Local Runtime

Bound directly to the underlying SeraphByte engine socket with zero middleware layers in between.

Custom Fine-tune

Trained on specialized dataset checkpoints to mirror distinct analytical preferences and reactions.

Weights Stack

Where persona meets metal.

Seraphine's responses travel straight from the fine-tuned tensors through SeraphByte's raw inference loop.

Client Interface (WebSocket) ws

SeraphByte Inference Server engine

Fine-tuned Attention Layers rust

Seraphine Quantized Weights (GGUF) weights

Custom BPE Tokenizer rust

Consumer Hardware (GPU/CPU) hw

            
            
            
            seraphine/weights.config
          
// active persona weight profile
{
    "name": "Seraphine",
    "base_model": "Llama-3-8B-Instruct",
    "checkpoint": "seraphine-v0.4-q4",
    "quantization": "Q4_K_M",
    "temperature": 0.7,
    "top_p": 0.9,
    "repetition_penalty": 1.15,
    "alignment_mode": "direct_weight"
}

✓ Zero runtime prompt injection overhead

✓ Responses streamed straight from tensor output

✓ Fully contained on local hardware

✓ Powered entirely by the SeraphByte backend

Engine Bridge

Ready to hook up your own model?

Seraphine runs on top of SeraphByte. Head over to the core engine repository details if you want to deploy your own custom weights.

Explore SeraphByte Engine ← Back to Vaelora