Branch: escha-w2-dense @ 2940b80. File: Escha-Qwen3.8-27B-W2-Q8E.gguf (2054 tensors, card-verified).
Load fails immediately:
llama_model_load: error loading model: check_tensor_dims: tensor 'blk.0.ssm_out.weight' not found
The file has no .weight tensor for ssm_out (or any matmul). Block 0 ships 32 tensors; the projections use Escha-native triplets:
- blk.0.ssm_out.escha_code / .escha_rin / .escha_rout / .bias
- same pattern for attn_qkv, attn_gate, ffn_gate/up/down
So the dense-branch SSM mapping still queries the classic weight name while the file (like the F16 build, 2052/2054 byte-identical per the model card) stores escha triplets. Attention/ffn escha paths load; the SSM block does not get that far.
Repro: stock run flags from the model card (-ngl 99 -fa on --jinja, q8_0 KV) on RTX 3060 12 GB, CUDA build. Ask: map the ssm_* escha triplets in the dense path, or confirm which file revision this branch loads.
Side note for 12 GB users: the card's own table puts 64k ctx at 12.31 GiB total — over a 12 GB card. 32k (11.25 GiB) is the realistic fit line there.
Branch: escha-w2-dense @ 2940b80. File: Escha-Qwen3.8-27B-W2-Q8E.gguf (2054 tensors, card-verified).
Load fails immediately:
The file has no
.weighttensor for ssm_out (or any matmul). Block 0 ships 32 tensors; the projections use Escha-native triplets:So the dense-branch SSM mapping still queries the classic weight name while the file (like the F16 build, 2052/2054 byte-identical per the model card) stores escha triplets. Attention/ffn escha paths load; the SSM block does not get that far.
Repro: stock run flags from the model card (-ngl 99 -fa on --jinja, q8_0 KV) on RTX 3060 12 GB, CUDA build. Ask: map the ssm_* escha triplets in the dense path, or confirm which file revision this branch loads.
Side note for 12 GB users: the card's own table puts 64k ctx at 12.31 GiB total — over a 12 GB card. 32k (11.25 GiB) is the realistic fit line there.