lelapa

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InkubaLM-0.4B

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Lelapa AI 31

Firstly, thanks for the model - very cool initiative!

I'm trying to port the model into a gguf file for use by tools such as llama.cpp and ollama. llama.cpp provides a script for doing this, which introspects the hugging face repo and creates a gguf file.

If I run it as is, and then run the model via llama.cpp's CLI, I get the following error:

llama_model_load: error loading model: check_tensor_dims: tensor 'blk.0.attn_k.weight' has wrong shape; expected  2048,   256, got  2048,  2048,     1,     1

which seems to suggest the attention parameters aren't internally consist, or the llama.cpp script is inferring the wrong values.

If I modify the parameter num_key_value_heads in this repo's config.json from 32 to 4, then llama.cpp will load up the model, and run it. However, It doesn't seem to return anything too sensible:

$ ./llama.cpp/llama-cli -m ./InkubaLM-0.4B/InkubaLM-0.4B-F32.gguf -p "Today i planned to " --temp 1 -n 10

Log start
main: build = 3568 (a21c6fd4)
main: built with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
main: seed  = 1723400004
llama_model_loader: loaded meta data with 33 key-value pairs and 75 tensors from ./InkubaLM-0.4B/InkubaLM-0.4B-F32.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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Vulavula_Config
llama_model_loader: - kv   3:                           general.basename str              = InkubaLM
llama_model_loader: - kv   4:                         general.size_label str              = 0.4B
llama_model_loader: - kv   5:                            general.license str              = cc-by-nc-4.0
llama_model_loader: - kv   6:                               general.tags arr[str,6]       = ["nlp", "InkubaLM", "africanLLM", "af...
llama_model_loader: - kv   7:                          general.languages arr[str,6]       = ["en", "sw", "zu", "xh", "ha", "yo"]
llama_model_loader: - kv   8:                           general.datasets arr[str,1]       = ["lelapa/Inkuba-Mono"]
llama_model_loader: - kv   9:                          llama.block_count u32              = 8
llama_model_loader: - kv  10:                       llama.context_length u32              = 2048
llama_model_loader: - kv  11:                     llama.embedding_length u32              = 2048
llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 5632
llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 4
llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 4
llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  17:                          general.file_type u32              = 0
llama_model_loader: - kv  18:                           llama.vocab_size u32              = 61788
llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 512
llama_model_loader: - kv  20:            tokenizer.ggml.add_space_prefix bool             = true
llama_model_loader: - kv  21:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  22:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  23:                      tokenizer.ggml.tokens arr[str,61788]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  24:                      tokenizer.ggml.scores arr[f32,61788]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,61788]   = [3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  26:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  27:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  28:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  29:            tokenizer.ggml.padding_token_id u32              = 2
llama_model_loader: - kv  30:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  31:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  32:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   75 tensors
llm_load_vocab: special tokens cache size = 3
llm_load_vocab: token to piece cache size = 0.3459 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 61788
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 2048
llm_load_print_meta: n_embd           = 2048
llm_load_print_meta: n_layer          = 8
llm_load_print_meta: n_head           = 4
llm_load_print_meta: n_head_kv        = 4
llm_load_print_meta: n_rot            = 512
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 512
llm_load_print_meta: n_embd_head_v    = 512
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 2048
llm_load_print_meta: n_embd_v_gqa     = 2048
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 5632
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 2048
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = ?B
llm_load_print_meta: model ftype      = all F32
llm_load_print_meta: model params     = 664.16 M
llm_load_print_meta: model size       = 2.47 GiB (32.00 BPW) 
llm_load_print_meta: general.name     = Vulavula_Config
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 2 '</s>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 2 '</s>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size =    0.04 MiB
llm_load_tensors:        CPU buffer size =  2533.57 MiB
..............................................
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =   128.00 MiB
llama_new_context_with_model: KV self size  =  128.00 MiB, K (f16):   64.00 MiB, V (f16):   64.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.24 MiB
llama_new_context_with_model:        CPU compute buffer size =   124.68 MiB
llama_new_context_with_model: graph nodes  = 262
llama_new_context_with_model: graph splits = 1

system_info: n_threads = 4 / 8 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
sampling: 
    repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
    top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 1.000
    mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order: 
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature 
generate: n_ctx = 2048, n_batch = 2048, n_predict = 10, n_keep = 1


 Today i planned to GHmgro ge ge ge ge ge ge ge
llama_print_timings:        load time =     492.17 ms
llama_print_timings:      sample time =       1.40 ms /    10 runs   (    0.14 ms per token,  7168.46 tokens per second)
llama_print_timings: prompt eval time =     170.29 ms /     6 tokens (   28.38 ms per token,    35.23 tokens per second)
llama_print_timings:        eval time =    1282.36 ms /     9 runs   (  142.48 ms per token,     7.02 tokens per second)
llama_print_timings:       total time =    1456.98 ms /    15 tokens
Log end

The output is Today i planned to GHmgro ge ge ge ge ge ge ge (it's a little buried in the log).

Firstly, is this a fundamentally bad idea?

Secondly, assuming the previous answer is some version of No, anything that I'm obviously doing wrong?