Model Zoo

Introduced: v1.0.0

Overview

The Model Zoo provides preset configurations and weight management for 5 supported model families. Configurations are defined in include/neural_core/kernel/model_zoo.h and implemented in kernel/model_zoo.c.

Supported architectures

Family	Sizes	Key features
LLaMA 2	7B, 13B, 70B	RoPE, GQA (70B), SwiGLU
LLaMA 3	8B, 70B	Scaled RoPE, higher theta, GQA
Mistral	7B	Sliding window (4096), GQA
Qwen 2	7B, 72B	Rope scaling (YARN/NTK), DCA
DeepSeek V2	Lite, Full	MLA (Multi-Head Latent Attention), MoE

Using the model zoo

C API

SNEPPXLLMConfig cfg;
SNEPPX_llm_config_from_name("llama3", "8B", &cfg);
// cfg now holds LLaMA 3 8B configuration

Python API

import sneppx

# Create config from name
cfg = sneppx.LLMConfig.from_name("deepseek_v2", "Full")
print(cfg.hidden_size)  # 5120

# Serialize to JSON
json_str = cfg.to_json()
print(json_str)

# Load from JSON
cfg2 = sneppx.LLMConfig.from_json(json_str)

Configuration fields

Each architecture has a dedicated config struct. Common fields across all architectures:

Field	Type	Description
hidden_size	size_t	Embedding dimension
intermediate_size	size_t	FFN intermediate dimension
numhiddenlayers	size_t	Number of transformer blocks
numattentionheads	size_t	Number of query heads
numkeyvalue_heads	size_t	KV heads for GQA
vocab_size	size_t	Vocabulary size
maxpositionembeddings	size_t	Base maximum sequence length
rmsnormeps	float	RMSNorm epsilon
rope_theta	float	RoPE theta base frequency
head_dim	int	Per-head dimension

Weight management

// Get weight name prefix
const char* prefix = SNEPPX_llm_weight_prefix(SNEPPX_MODEL_LLAMA_3);
// prefix == "model.layers"

// Get total number of weight tensors
int count = SNEPPX_llm_num_weight_tensors(SNEPPX_MODEL_LLAMA_3, 32);

Verification

See tests/test_model_zoo.c for tests covering: - All named preset sizes resolve successfully - JSON round-trip (serialize → parse → verify fields) - All weight name prefixes match expected values - Context extension integration