The development build will either crash or produce incorrect output content. about llmfarm HOT 11 CLOSED

And I also confirmed that I've used these models with the correct settings template.

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Well, if you run LLMFarm from XCode you should be able to see where the error occurs. I would be very grateful if you could send me a more detailed description of the errors.

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Well, although some crashes don't show errors, I tried to capture some logs while it was crashing:
ex.
tinnyllama-1.1b: Metal is on / MLock is off / Mmap is on

AI init
llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from /var/mobile/Containers/Data/Application/03B8B998-F4AA-4BF3-9E0E-A82E061A1CC1/Documents/models/tinyllama-1.1b-chat-v1.0.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 = llama
llama_model_loader: - kv 1: general.name str = tinyllama_tinyllama-1.1b-chat-v1.0
llama_model_loader: - kv 2: llama.context_length u32 = 2048
llama_model_loader: - kv 3: llama.embedding_length u32 = 2048
llama_model_loader: - kv 4: llama.block_count u32 = 22
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 5632
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 64
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 4
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 11: general.file_type u32 = 7
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,61249] = ["▁ t", "e r", "i n", "▁ a", "e n...
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.chat_template str = {% for message in messages %}\n{% if m...
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - type f32: 45 tensors
llama_model_loader: - type q8_0: 156 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 = 2048
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_layer = 22
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
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 = 5632
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 = 2048
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 1B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 1.10 B
llm_load_print_meta: model size = 1.09 GiB (8.50 BPW)
llm_load_print_meta: general.name = tinyllama_tinyllama-1.1b-chat-v1.0
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: PAD token = 2 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.15 MiB
ggml_backend_metal_buffer_from_ptr: allocated buffer, size = 1114.92 MiB, ( 1115.00 / 5461.34)
llm_load_tensors: offloading 22 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 23/23 layers to GPU
llm_load_tensors: CPU buffer size = 66.41 MiB
llm_load_tensors: Metal buffer size = 1114.92 MiB
llama_new_context_with_model: n_ctx = 1024
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: picking default device: Apple A17 Pro GPU
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/var/containers/Bundle/Application/13501DF2-09F1-454E-B12B-62CE7A418F5D/LLMFarm.app/llmfarm_core_llmfarm_core_cpp.bundle/metal/ggml-metal.metal'
ggml_metal_init: GPU name: Apple A17 Pro GPU
ggml_metal_init: GPU family: MTLGPUFamilyApple9 (1009)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 5726.63 MB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 22.00 MiB, ( 1139.88 / 5461.34)
llama_kv_cache_init: Metal KV buffer size = 22.00 MiB
llama_new_context_with_model: KV self size = 22.00 MiB, K (f16): 11.00 MiB, V (f16): 11.00 MiB
llama_new_context_with_model: CPU input buffer size = 6.01 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 82.02 MiB, ( 1221.89 / 5461.34)
llama_new_context_with_model: Metal compute buffer size = 82.00 MiB
llama_new_context_with_model: CPU compute buffer size = 4.00 MiB
llama_new_context_with_model: graph splits (measure): 3
%s: seed = %d
0
AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 |
Logits inited.
ModelSampleParams(n_batch: 512, temp: 0.9, top_k: 40, top_p: 0.95, tfs_z: 1.0, typical_p: 1.0, repeat_penalty: 1.1, repeat_last_n: 64, frequence_penalty: 0.0, presence_penalty: 0.0, mirostat: 0, mirostat_tau: 5.0, mirostat_eta: 5.0, penalize_nl: true)
ModelAndContextParams(model_inference: llmfarm_core.ModelInference.LLama_gguf, context: 1024, parts: -1, seed: 4294967295, n_threads: 6, lora_adapters: [], promptFormat: llmfarm_core.ModelPromptStyle.Custom, custom_prompt_format: "<|user|>{prompt}\n<|assistant|>", system_prompt: "You are a story writing assistant.", f16Kv: true, logitsAll: false, vocabOnly: false, useMlock: false, useMMap: true, embedding: false, processorsConunt: 6, use_metal: true, grammar_path: nil, add_bos_token: false, add_eos_token: false, parse_special_tokens: false, warm_prompt: "\n\n\n", reverse_prompt: [], clip_model: nil)

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And here is the incorrect content example:
Orca-mini-3b on iPhone 15 Pro Max Simulator

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Is this incorrect output in version 1.0.1 or earlier?

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output of versions before 1.0.1 may be very different due to changes in llama.cpp

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metal does not work in the simulator and since version 1.0.0 it is disabled there

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The incorrect output is in earlier version (0.9.0 and 1.0.0). I will try it out with 1.0.1. Thanks for the information.

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Hi @guinmoon ,

I've updated to 1.0.1 version, but I'm still getting the incorrect response, Could you please help me understand why or suggest anything I need to fine-tune? Thank you.

iPhone 15 Pro Max Simulator:
Phi2

Orca-mini-3b

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i use this template

[System](You are a helpful, respectful and honest assistant. Always answer as helpfully as possible.)
Instruct: {prompt}
Output:

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Thanks for the template. I will try it out.

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