Description
I cloned the llama cpp repo and tried to finetune vicuna 7B with this script :
./finetune --model-base models/vicuna-model-q4_0.gguf --train-data ~mostafa/llama.cpp/txt.txt --lora-out lora.gguf --save-every 0 --threads 12 --ctx 4096 --batch 1 --grad-acc 1 --adam-iter 256 --adam-alpha 0.001 --lora-r 4 --lora-alpha 4 --use-checkpointing --use-flash --sample-start ""
but I got this output:
main: seed: 1705223295
main: model base = 'models/ggml-model-q4_0-v2.gguf'
llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from models/ggml-model-q4_0-v2.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.name str = LLaMA v2
llama_model_loader: - kv 2: llama.context_length u32 = 4096
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
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 = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
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: n_ff = 11008
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 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_yarn_orig_ctx = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 3.56 GiB (4.54 BPW)
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 '''
llm_load_print_meta: EOS token = 2 '
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors: CPU buffer size = 3647.87 MiB
..................................................................................................
llama_new_context_with_model: n_ctx = 512
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 = 256.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: graph splits (measure): 1
llama_new_context_with_model: CPU compute buffer size = 70.50 MiB
main: init model
print_params: n_vocab : 32000
print_params: n_ctx : 4000
print_params: n_embd : 4096
print_params: n_ff : 11008
print_params: n_head : 32
print_params: n_head_kv : 32
print_params: n_layer : 32
print_params: norm_rms_eps : 0.000010
print_params: rope_freq_base : 10000.000000
print_params: rope_freq_scale : 1.000000
print_lora_params: n_rank_attention_norm : 1
print_lora_params: n_rank_wq : 4
print_lora_params: n_rank_wk : 4
print_lora_params: n_rank_wv : 4
print_lora_params: n_rank_wo : 4
print_lora_params: n_rank_ffn_norm : 1
print_lora_params: n_rank_w1 : 4
print_lora_params: n_rank_w2 : 4
print_lora_params: n_rank_w3 : 4
print_lora_params: n_rank_tok_embeddings : 4
print_lora_params: n_rank_norm : 1
print_lora_params: n_rank_output : 4
main: total train_iterations 0
main: seen train_samples 0
main: seen train_tokens 0
main: completed train_epochs 0
main: lora_size = 84863776 bytes (80.9 MB)
main: opt_size = 126593008 bytes (120.7 MB)
main: opt iter 0
main: input_size = 4096128032 bytes (3906.4 MB)
main: compute_size = 164342300384 bytes (156729.0 MB)
main: evaluation order = LEFT_TO_RIGHT
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)
with this error : terminate called after throwing an instance of 'std::bad_alloc'