Bug: Gemma-2 not supported on b3262

[nmandic78]

What happened?

I pulled and built b3262, but when loading the model (both server and cli) I get response that gemma2 is unknown architecture.
$ git log -1 --oneline
38373cf (HEAD -> master, tag: b3262, origin/master, origin/HEAD) Add SPM infill support (#8016)

Looking at release notes, I expected it to be supported from 2 releases before:
b3259
llama: Add support for Gemma2ForCausalLM (#8156)
Inference support for Gemma 2 model family

Am I missing something (as I don't see anybody else complaining)?

Name and Version

version: 3262 (38373cf)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu

What operating system are you seeing the problem on?

Linux

Relevant log output

$ ./server -m /mnt/disk2/LLM_MODELS/models/gemma-2-9b-it-Q8_0.gguf -ngl 99 -c 4096
{"tid":"131173750738944","timestamp":1719588568,"level":"INFO","function":"main","line":2940,"msg":"build info","build":2964,"commit":"9b3d8331"}
{"tid":"131173750738944","timestamp":1719588568,"level":"INFO","function":"main","line":2945,"msg":"system info","n_threads":6,"n_threads_batch":-1,"total_threads":6,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "}
llama_model_loader: loaded meta data with 25 key-value pairs and 464 tensors from /mnt/disk2/LLM_MODELS/models/gemma-2-9b-it-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = gemma2
llama_model_loader: - kv   1:                               general.name str              = Gemma2 9B
llama_model_loader: - kv   2:                      gemma2.context_length u32              = 8192
llama_model_loader: - kv   3:                         gemma2.block_count u32              = 42
llama_model_loader: - kv   4:                    gemma2.embedding_length u32              = 3584
llama_model_loader: - kv   5:                 gemma2.feed_forward_length u32              = 14336
llama_model_loader: - kv   6:                gemma2.attention.head_count u32              = 16
llama_model_loader: - kv   7:             gemma2.attention.head_count_kv u32              = 8
llama_model_loader: - kv   8:                gemma2.attention.key_length u32              = 256
llama_model_loader: - kv   9:              gemma2.attention.value_length u32              = 256
llama_model_loader: - kv  10:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  12:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  13:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  14:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  15:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,256128]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,256128]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,256128]  = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  19:               general.quantization_version u32              = 2
llama_model_loader: - kv  20:                          general.file_type u32              = 7
llama_model_loader: - kv  21:                      quantize.imatrix.file str              = /models/gemma-2-9b-it-GGUF/gemma-2-9b...
llama_model_loader: - kv  22:                   quantize.imatrix.dataset str              = /training_data/calibration_datav3.txt
llama_model_loader: - kv  23:             quantize.imatrix.entries_count i32              = 294
llama_model_loader: - kv  24:              quantize.imatrix.chunks_count i32              = 128
llama_model_loader: - type  f32:  169 tensors
llama_model_loader: - type q8_0:  295 tensors
llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma2'
llama_load_model_from_file: failed to load model
llama_init_from_gpt_params: error: failed to load model '/mnt/disk2/LLM_MODELS/models/gemma-2-9b-it-Q8_0.gguf'
{"tid":"131173750738944","timestamp":1719588569,"level":"ERR","function":"load_model","line":692,"msg":"unable to load model","model":"/mnt/disk2/LLM_MODELS/models/gemma-2-9b-it-Q8_0.gguf"}
free(): invalid pointer
Aborted (core dumped)

[bartowski1182]

can you show your build log? i can confirm it works for me (though not specifically b3262, will update and verify), so wondering if it cached some build stuff and you didn't actually get latest

[MoonRide303]

gemma-2-9b-it works fine for me - Q6_K quant, converted and launched using llama.cpp b3259.

[slaren]

The name of the server binary has been changed to llama-server, you are probably using an old build.

[brittlewis12]

@nmandic78 server & main have been renamed as of #7809. you may inadvertently be using stale compilation artifacts.

https://github.com/ggerganov/llama.cpp/blob/26a39bbd6b0bbd66118bb68569f0276d7fe7df6c/README.md?plain=1#L14

try llama-server & llama-cli instead

[nmandic78]

Oh, I feel so stupid now :D Should have read that.
Indeed, when using renamed (ones really built with new release) binaries, there is no problem.

Thank you!

[werruww]

gemma 9b not work

lama.cpp b3259
pip install git+https://github.com/ggerganov/llama.cpp.git@b3259

llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma2'
llama_load_model_from_file: failed to load model
Traceback (most recent call last):
File "C:\Users\m\Desktop\ollama\1.py", line 4, in
llm = Llama(
^^^^^^
File "C:\Users\m\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp\llama.py", line 358, in init
self._model = self._stack.enter_context(contextlib.closing(_LlamaModel(
^^^^^^^^^^^^
File "C:\Users\m\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_cpp_internals.py", line 54, in init
raise ValueError(f"Failed to load model from file: {path_model}")
ValueError: Failed to load model from file: ./gemma-2-9b-it.Q4_K.gguf

[wesleysanjose]

i still see following when running gemma2 27b q4_k_m of https://huggingface.co/bartowski/gemma-2-27b-it-GGUF after updating to latest commits

llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma2'

[MoonRide303]

i still see following when running gemma2 27b q4_k_m of https://huggingface.co/bartowski/gemma-2-27b-it-GGUF after updating to latest commits

llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma2'

I just tested build from current master (d0a7145) using freshly downloaded Q3_K_S quant from this repo, launched like this:
llama-server.exe -v -ngl 99 -m gemma-2-27b-it-Q3_K_S.gguf -c 4096
and it seems to be working (at least in basic scope - stuff like interleaved SWA/full attention might be still missing):

[bartowski1182]

@wesleysanjose make sure you're using llama-cli and have all the latest binaries

[wesleysanjose]

@bartowski1182 I use server to launch openai compatible server, what's the difference? I always uses that

./server -m $1 --n-gpu-layers $2 -c $3 --host 192.168.0.184 --port 5000 -b 4096 -to 120 -ts 20,6