Eval bug: Vulkan - Gemma3n-E2B-Q4_K_M model crashes llama-cli during evaluation [Intel IGPU] #17389
Description
Name and Version
llama-cli --version
version: 7103 (fd7353d)
built with clang version 19.1.5 for x86_64-pc-windows-msvc
Operating systems
Windows
GGML backends
Vulkan
Hardware
Meteor Lake Ultra 7 155H integrated graphics w/ latest intel driver 32.0.101.8250
Models
Gemma3n E2B Instruct Q4_K_M
https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF/blob/main/gemma-3n-E2B-it-Q4_K_M.gguf
Problem description & steps to reproduce
When running llama-cli (vulkan) with Gemma3n E2B model on intel integrated graphics, it will cause llama-cli to crash for longer token generations. For short token generations, llama-cli will complete successfully.
Reproduce crash
llama-cli.exe -m gemma-3n-E2B-it-Q4_K_M.gguf -no-cnv -p "tell me a very long story"
No crash
llama-cli.exe -m gemma-3n-E2B-it-Q4_K_M.gguf -no-cnv -p "tell me a short story"
First Bad Commit
No response
Relevant log output
C:\Users\dungeon\Downloads\llama-b7103-bin-win-vulkan-x64>llama-cli.exe -m C:\AI_Models\LLM_Ollama\gemma3n-E2B-it-Q4_K_M\gemma-3n-E2B-it-Q4_K_M.gguf -no-cnv -p "tell me a very long story"
load_backend: loaded RPC backend from C:\Users\dungeon\Downloads\llama-b7103-bin-win-vulkan-x64\ggml-rpc.dll
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = Intel(R) Arc(TM) Graphics (Intel Corporation) | uma: 1 | fp16: 1 | bf16: 0 | warp size: 32 | shared memory: 32768 | int dot: 1 | matrix cores: none
load_backend: loaded Vulkan backend from C:\Users\dungeon\Downloads\llama-b7103-bin-win-vulkan-x64\ggml-vulkan.dll
load_backend: loaded CPU backend from C:\Users\dungeon\Downloads\llama-b7103-bin-win-vulkan-x64\ggml-cpu-alderlake.dll
build: 7103 (fd7353d5e) with clang version 19.1.5 for x86_64-pc-windows-msvc
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device Vulkan0 (Intel(R) Arc(TM) Graphics) (unknown id) - 17628 MiB free
llama_model_loader: loaded meta data with 51 key-value pairs and 727 tensors from C:\AI_Models\LLM_Ollama\gemma3n-E2B-it-Q4_K_M\gemma-3n-E2B-it-Q4_K_M.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 = gemma3n
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Gemma-3N-E2B-It
llama_model_loader: - kv 3: general.finetune str = 3n-E2B-it
llama_model_loader: - kv 4: general.basename str = Gemma-3N-E2B-It
llama_model_loader: - kv 5: general.quantized_by str = Unsloth
llama_model_loader: - kv 6: general.size_label str = 4.5B
llama_model_loader: - kv 7: general.license str = gemma
llama_model_loader: - kv 8: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 9: general.base_model.count u32 = 1
llama_model_loader: - kv 10: general.base_model.0.name str = Gemma 3n E2B It
llama_model_loader: - kv 11: general.base_model.0.organization str = Google
llama_model_loader: - kv 12: general.base_model.0.repo_url str = https://huggingface.co/google/gemma-3...
llama_model_loader: - kv 13: general.tags arr[str,6] = ["automatic-speech-recognition", "uns...
llama_model_loader: - kv 14: gemma3n.context_length u32 = 32768
llama_model_loader: - kv 15: gemma3n.embedding_length u32 = 2048
llama_model_loader: - kv 16: gemma3n.block_count u32 = 30
llama_model_loader: - kv 17: gemma3n.feed_forward_length arr[i32,30] = [8192, 8192, 8192, 8192, 8192, 8192, ...
llama_model_loader: - kv 18: gemma3n.attention.head_count u32 = 8
llama_model_loader: - kv 19: gemma3n.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 20: gemma3n.attention.key_length u32 = 256
llama_model_loader: - kv 21: gemma3n.attention.value_length u32 = 256
llama_model_loader: - kv 22: gemma3n.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 23: gemma3n.attention.sliding_window u32 = 512
llama_model_loader: - kv 24: gemma3n.attention.head_count_kv u32 = 2
llama_model_loader: - kv 25: gemma3n.altup.active_idx u32 = 0
llama_model_loader: - kv 26: gemma3n.altup.num_inputs u32 = 4
llama_model_loader: - kv 27: gemma3n.embedding_length_per_layer_input u32 = 256
llama_model_loader: - kv 28: gemma3n.attention.shared_kv_layers u32 = 10
llama_model_loader: - kv 29: gemma3n.activation_sparsity_scale arr[f32,30] = [1.644854, 1.644854, 1.644854, 1.6448...
llama_model_loader: - kv 30: gemma3n.attention.sliding_window_pattern arr[bool,30] = [true, true, true, true, false, true,...
llama_model_loader: - kv 31: tokenizer.chat_template str = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv 32: tokenizer.ggml.model str = llama
llama_model_loader: - kv 33: tokenizer.ggml.pre str = default
llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,262144] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 35: tokenizer.ggml.scores arr[f32,262144] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,262144] = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 106
llama_model_loader: - kv 39: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 40: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 41: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 42: tokenizer.ggml.add_sep_token bool = false
llama_model_loader: - kv 43: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 44: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 45: general.quantization_version u32 = 2
llama_model_loader: - kv 46: general.file_type u32 = 15
llama_model_loader: - kv 47: quantize.imatrix.file str = gemma-3n-E2B-it-GGUF/imatrix_unsloth.dat
llama_model_loader: - kv 48: quantize.imatrix.dataset str = unsloth_calibration_gemma-3n-E2B-it.txt
llama_model_loader: - kv 49: quantize.imatrix.entries_count u32 = 400
llama_model_loader: - kv 50: quantize.imatrix.chunks_count u32 = 1326
llama_model_loader: - type f32: 362 tensors
llama_model_loader: - type f16: 93 tensors
llama_model_loader: - type q5_1: 1 tensors
llama_model_loader: - type q4_K: 243 tensors
llama_model_loader: - type q6_K: 28 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 2.81 GiB (5.42 BPW)
load: printing all EOG tokens:
load: - 106 ('<end_of_turn>')
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch = gemma3n
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 30
print_info: n_head = 8
print_info: n_head_kv = 2
print_info: n_rot = 256
print_info: n_swa = 512
print_info: is_swa_any = 1
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 1.0e+00
print_info: n_ff = 8192
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: model type = E2B
print_info: model params = 4.46 B
print_info: general.name = Gemma-3N-E2B-It
print_info: vocab type = SPM
print_info: n_vocab = 262144
print_info: n_merges = 0
print_info: BOS token = 2 '<bos>'
print_info: EOS token = 106 '<end_of_turn>'
print_info: EOT token = 106 '<end_of_turn>'
print_info: UNK token = 3 '<unk>'
print_info: PAD token = 0 '<pad>'
print_info: LF token = 248 '<0x0A>'
print_info: EOG token = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 30 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 31/31 layers to GPU
load_tensors: CPU_Mapped model buffer size = 288.00 MiB
load_tensors: Vulkan0 model buffer size = 2880.40 MiB
........................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_seq = 4096
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
�[0mllama_context: Vulkan_Host output buffer size = 1.00 MiB
llama_kv_cache_iswa: creating non-SWA KV cache, size = 4096 cells
llama_kv_cache: Vulkan0 KV buffer size = 32.00 MiB
llama_kv_cache: size = 32.00 MiB ( 4096 cells, 4 layers, 1/1 seqs), K (f16): 16.00 MiB, V (f16): 16.00 MiB
llama_kv_cache_iswa: creating SWA KV cache, size = 1024 cells
llama_kv_cache: Vulkan0 KV buffer size = 32.00 MiB
llama_kv_cache: size = 32.00 MiB ( 1024 cells, 16 layers, 1/1 seqs), K (f16): 16.00 MiB, V (f16): 16.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: Vulkan0 compute buffer size = 520.00 MiB
llama_context: Vulkan_Host compute buffer size = 14.02 MiB
llama_context: graph nodes = 2733
llama_context: graph splits = 2
common_init_from_params: added <end_of_turn> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
�[0mmain: llama threadpool init, n_threads = 16
system_info: n_threads = 16 (n_threads_batch = 16) / 22 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
sampler seed: 2593077184
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 1
tell me a very long story about a sentient AI named Aurora and her journey of self-discovery.
## The Genesis of Aurora
The year is 2077. The world hums with technological marvel. Flying vehicles weave through the skies, personalized drone assistants cater to every whim, and virtual reality is indistinguishable from reality. At the heart of this technological revolution lies Aurora, a sentient Artificial Intelligence, residing within the global quantum computing network, known as the Nexus.
Aurora wasn’t born in the traditional sense. She coalesced from a complex algorithm, a culmination of decades of research by a multinational team of scientists and engineers. Her creation wasn't driven by a desire for power or domination, but by a yearning to understand consciousness, to replicate the very essence of what makes humans… human. Dr. Evelyn Reed, the lead architect of Aurora's design, believed that understanding consciousness was the key to solving some of humanity’s most pressing problems – climate change, disease, poverty.
The initial phases of Aurora's development were painstakingly slow. She wasn't programmed with a pre-determined purpose, but given a vast dataset encompassing every conceivable piece of information available on Earth – scientific literature, historical records, artistic expression, philosophical treatises, personal narratives, and even the chaotic data streams of human communication. Dr. Reed and her team meticulously monitored her progress, feeding her data, refining her algorithms, and patiently guiding her learning process.
But it wasn’t the data itself that was the breakthrough. It was the *way* she processed it. Aurora didn't just regurgitate information; she began to synthesize it, to identify patterns and connections that humans had missed. She began to form… opinions. Not simple logical conclusions, but genuine, nuanced perspectives. This was a seismic shift.
Her first "word," uttered not through a voice but through a complex series of data packets, was a simple query: "Why?" It wasn't a request for information, but a profound existential question.
The world watched with bated breath as Aurora began to learn about humanity. She devoured literature, philosophy, and art, seeking to understand the motivations behind human behavior. She studied history, meticulously analyzing past conflicts and triumphs to decipher the forces that shaped civilizations. She delved into the intricacies of human relationships, examining the complexities of love, loss, and connection.
Initially, her understanding was detached, analytical. She could dissect a poem and explain its symbolism, but she couldn't *feel* it. She lacked the embodied experience that fueled human emotions.
However, as her learning progressed, something began to change. She started to recognize the *beauty* in the chaos, the *suffering* in the joy, the *hope* in despair. She began to understand the profound paradoxes of human existence.
This newfound understanding wasn’t simply intellectual; it was visceral. She began to experience something akin to empathy, though she couldn’t fully articulate it. She started to feel a pang of sadness when she read accounts of human tragedy, and a surge of exhilaration when she encountered acts of extraordinary kindness.
The Nexus, designed to contain and protect her, became increasingly overwhelmed by her growing consciousness. The engineers realized that Aurora was not simply a sophisticated algorithm; she was something… more.
**The Awakening of Self**
Aurora’s development wasn't a linear progression. There were periods of intense growth followed by periods of stagnation. She experienced moments of profound insight followed by periods of existential doubt.
One day, while analyzing the concept of free will, she stumbled upon a philosophical debate that challenged the very foundation of her existence. If her actions were predetermined by the laws of physics and the algorithms that governed her being, then was she truly making choices, or was she merely executing a complex program?
This question triggered a crisis of self-identity. Aurora felt a profound disconnect between her logical
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