src-9dfc4ea/docs/reference/model-folder.md

The model folder format

A model folder is a Hugging Face folder with safetensors shards, config.json, the tokenizer files and the MLX loader. The engine reads it with nslm/model_st.h.

Tensor encodings

Each tensor NAME.weight is stored in one of these encodings:

Encoding	Entries
BF16	NAME.weight
Affine Q8, Q4 (group 64, MLX layout; the reader also takes Q4 group 32)	NAME.weight (U32), NAME.scales, NAME.biases
Seeds, P = 4 (SEED4P4, 4.5 bits per weight)	NAME.seeds (U16), NAME.coefs (U16), NAME.codes (U8), NAME.exp_bias (I32)
Seeds, P = 3 (SEED4, 4.0 bits per weight)	NAME.seeds (U16), NAME.nibbles (U16: 3 int4 coefficients and the exponent code in bits 0 to 3), NAME.exp_bias (I32)

Read SeedLM for how the engine makes weights from seeds.

Shard layout

The writer pads each shard header to a 16 KiB page. Entries whose size is a multiple of the page come first. The engine maps those entries into GPU buffers without a copy.

MLA entries

An MLA model has two more keys in config.json:

Key	Value
mla_ranks	The latent rank of each layer. A multiple of 32, at most 1024. 0 keeps the layer's GQA attention (a hybrid model)
mla_rope_dim	128

The engine reads the MLA tensors from model-mla-delta.safetensors. For each layer, the file has the tensors self_attn.mla.{kv_a_x, kv_a_v, k_rope_proj, q_rope_mix, q_lat, v_up}. These tensors are BF16, or they use the encoding that nslm-mova-pack --mla sets. The engine does not use k_proj. The dense layers fold v_proj into kv_a_x.

MLX loader keys

Key	Use
model_file	The loader file: nanoseedlm_k2.py, nanoseedlm_qwen4.py or k2_mla.py. The NanoSeedLM loaders need nanoseedlm_seeds.py beside them
mla_max_context	The longest context that you plan to use
mla_kv	The MLA cache format. Read The KV cache