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[GH-ISSUE #7443] Reply GGGGGGGGGGGGGG running nemotron:latest #82411

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opened 2026-05-09 14:05:06 -05:00 by GiteaMirror · 4 comments
GiteaMirror commented 2026-05-09 14:05:06 -05:00
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Originally created by @3keyallen3 on GitHub (Oct 31, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/7443

What is the issue?

Ollama using Docker mode.
When execute 'sudo docker exec -it ollama ollama run nemotron:latest',
or "sudo docker exec -it ollama ollama run qwen2.5:72b"
it replied "GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG" all the time.

NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-SXM4-40GB Off | 00000000:02:00.0 Off | Off |
| N/A 25C P0 35W / 400W | 35011MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-SXM4-40GB Off | 00000000:61:00.0 Off | Off |
| N/A 26C P0 36W / 400W | 39833MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA A100-SXM4-40GB Off | 00000000:E1:00.0 Off | Off |
| N/A 27C P0 37W / 400W | 37159MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------

OS

Docker

GPU

Nvidia

CPU

Intel

Ollama version

0.3.14

Originally created by @3keyallen3 on GitHub (Oct 31, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/7443 ### What is the issue? Ollama using Docker mode. When execute 'sudo docker exec -it ollama ollama run nemotron:latest', or "sudo docker exec -it ollama ollama run qwen2.5:72b" it replied "GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG" all the time. NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA A100-SXM4-40GB Off | 00000000:02:00.0 Off | Off | | N/A 25C P0 35W / 400W | 35011MiB / 40960MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA A100-SXM4-40GB Off | 00000000:61:00.0 Off | Off | | N/A 26C P0 36W / 400W | 39833MiB / 40960MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 2 NVIDIA A100-SXM4-40GB Off | 00000000:E1:00.0 Off | Off | | N/A 27C P0 37W / 400W | 37159MiB / 40960MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+---------------------- ### OS Docker ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.14
GiteaMirror added the bug label 2026-05-09 14:05:06 -05:00
GiteaMirror commented 2026-05-09 14:05:09 -05:00
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@3keyallen3 commented on GitHub (Oct 31, 2024):

图片
Works well for qwen2.5:32b but qwen2.5:72b
Add OLLAMA_FLASH_ATTENTION=1 , still not working.

<!-- gh-comment-id:2449153649 --> @3keyallen3 commented on GitHub (Oct 31, 2024): ![图片](https://github.com/user-attachments/assets/caab501a-754e-417e-abc2-d25b1b088520) Works well for qwen2.5:32b but qwen2.5:72b Add OLLAMA_FLASH_ATTENTION=1 , still not working.
GiteaMirror commented 2026-05-09 14:05:10 -05:00
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@rick-github commented on GitHub (Oct 31, 2024):

Server logs might help with debugging.

$ docker exec -it ollama ollama -v
ollama version is 0.3.14
$ docker exec -it ollama ollama run qwen2.5:72b
>>> hello
Hello! How can I assist you today? Feel free to ask any questions or let me know if you need help with anything specific.

>>>
$ docker exec -it ollama ollama ps
NAME           ID              SIZE     PROCESSOR    UNTIL            
qwen2.5:72b    424bad2cc13f    57 GB    100% GPU     2 hours from now
<!-- gh-comment-id:2450955685 --> @rick-github commented on GitHub (Oct 31, 2024): [Server logs](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) might help with debugging. ```console $ docker exec -it ollama ollama -v ollama version is 0.3.14 $ docker exec -it ollama ollama run qwen2.5:72b >>> hello Hello! How can I assist you today? Feel free to ask any questions or let me know if you need help with anything specific. >>> $ docker exec -it ollama ollama ps NAME ID SIZE PROCESSOR UNTIL qwen2.5:72b 424bad2cc13f 57 GB 100% GPU 2 hours from now ```
GiteaMirror commented 2026-05-09 14:05:11 -05:00
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@3keyallen3 commented on GitHub (Nov 1, 2024):

@rick-github THX for reply.
Here is the log.
[GIN] 2024/11/01 - 00:40:13 | 200 | 32.178µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/11/01 - 00:40:13 | 200 | 15.146772ms | 127.0.0.1 | POST "/api/show"
time=2024-11-01T00:40:13.955Z level=INFO source=sched.go:730 msg="new model will fit in available VRAM, loading" model=/root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 library=cuda parallel=4 required="53.0 GiB"
time=2024-11-01T00:40:14.555Z level=INFO source=server.go:105 msg="system memory" total="2267.4 GiB" free="2236.9 GiB" free_swap="8.0 GiB"
time=2024-11-01T00:40:14.556Z level=INFO source=memory.go:326 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=81 layers.split=27,27,27 memory.available="[39.1 GiB 39.1 GiB 39.1 GiB]" memory.gpu_overhead="0 B" memory.required.full="53.0 GiB" memory.required.partial="53.0 GiB" memory.required.kv="2.5 GiB" memory.required.allocations="[18.6 GiB 17.1 GiB 17.3 GiB]" memory.weights.total="45.0 GiB" memory.weights.repeating="44.1 GiB" memory.weights.nonrepeating="974.6 MiB" memory.graph.full="1.3 GiB" memory.graph.partial="1.3 GiB"
time=2024-11-01T00:40:14.557Z level=INFO source=server.go:388 msg="starting llama server" cmd="/usr/lib/ollama/runners/cuda_v12/ollama_llama_server --model /root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 --ctx-size 8192 --batch-size 512 --embedding --n-gpu-layers 81 --threads 96 --flash-attn --parallel 4 --tensor-split 27,27,27 --port 37747"
time=2024-11-01T00:40:14.557Z level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-11-01T00:40:14.557Z level=INFO source=server.go:587 msg="waiting for llama runner to start responding"
time=2024-11-01T00:40:14.558Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error"
INFO [main] starting c++ runner | tid="140076011839488" timestamp=1730421614
INFO [main] build info | build=10 commit="b45ed63" tid="140076011839488" timestamp=1730421614
INFO [main] system info | n_threads=96 n_threads_batch=96 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140076011839488" timestamp=1730421614 total_threads=384
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="383" port="37747" tid="140076011839488" timestamp=1730421614
llama_model_loader: loaded meta data with 35 key-value pairs and 963 tensors from /root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 (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 = qwen2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen2.5 72B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Qwen2.5
llama_model_loader: - kv 5: general.size_label str = 72B
llama_model_loader: - kv 6: general.license str = other
llama_model_loader: - kv 7: general.license.name str = qwen
llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7...
llama_model_loader: - kv 9: general.base_model.count u32 = 1
llama_model_loader: - kv 10: general.base_model.0.name str = Qwen2.5 72B
llama_model_loader: - kv 11: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 12: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-72B
llama_model_loader: - kv 13: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: - kv 14: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 15: qwen2.block_count u32 = 80
llama_model_loader: - kv 16: qwen2.context_length u32 = 32768
llama_model_loader: - kv 17: qwen2.embedding_length u32 = 8192
llama_model_loader: - kv 18: qwen2.feed_forward_length u32 = 29568
llama_model_loader: - kv 19: qwen2.attention.head_count u32 = 64
llama_model_loader: - kv 20: qwen2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 21: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 22: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 23: general.file_type u32 = 15
llama_model_loader: - kv 24: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 25: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,152064] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 27: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 28: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 33: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 34: general.quantization_version u32 = 2
llama_model_loader: - type f32: 401 tensors
llama_model_loader: - type q5_0: 40 tensors
llama_model_loader: - type q8_0: 40 tensors
llama_model_loader: - type q4_K: 401 tensors
llama_model_loader: - type q5_K: 40 tensors
llama_model_loader: - type q6_K: 41 tensors
llm_load_vocab: special tokens cache size = 22
time=2024-11-01T00:40:14.809Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: token to piece cache size = 0.9310 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 152064
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_layer = 80
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 29568
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 = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 70B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 72.71 B
llm_load_print_meta: model size = 44.15 GiB (5.22 BPW)
llm_load_print_meta: general.name = Qwen2.5 72B Instruct
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: EOG token = 151643 '<|endoftext|>'
llm_load_print_meta: EOG token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 3 CUDA devices:
Device 0: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
Device 1: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
Device 2: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes
llm_load_tensors: ggml ctx size = 1.69 MiB
time=2024-11-01T00:40:16.264Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding"
time=2024-11-01T00:40:17.417Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model"
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 81/81 layers to GPU
llm_load_tensors: CPU buffer size = 668.25 MiB
llm_load_tensors: CUDA0 buffer size = 14836.62 MiB
llm_load_tensors: CUDA1 buffer size = 14310.50 MiB
llm_load_tensors: CUDA2 buffer size = 15398.08 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 864.00 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 864.00 MiB
llama_kv_cache_init: CUDA2 KV buffer size = 832.00 MiB
llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 2.45 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
llama_new_context_with_model: CUDA0 compute buffer size = 299.51 MiB
llama_new_context_with_model: CUDA1 compute buffer size = 259.51 MiB
llama_new_context_with_model: CUDA2 compute buffer size = 409.02 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 80.02 MiB
llama_new_context_with_model: graph nodes = 2487
llama_new_context_with_model: graph splits = 4
INFO [main] model loaded | tid="140076011839488" timestamp=1730421622
time=2024-11-01T00:40:22.841Z level=INFO source=server.go:626 msg="llama runner started in 8.28 seconds"
[GIN] 2024/11/01 - 00:40:22 | 200 | 9.618436111s | 127.0.0.1 | POST "/api/generate"
[GIN] 2024/11/01 - 00:40:28 | 200 | 1.891222246s | 127.0.0.1 | POST "/api/chat"
图片

<!-- gh-comment-id:2451083434 --> @3keyallen3 commented on GitHub (Nov 1, 2024): @rick-github THX for reply. Here is the log. [GIN] 2024/11/01 - 00:40:13 | 200 | 32.178µs | 127.0.0.1 | HEAD "/" [GIN] 2024/11/01 - 00:40:13 | 200 | 15.146772ms | 127.0.0.1 | POST "/api/show" time=2024-11-01T00:40:13.955Z level=INFO source=sched.go:730 msg="new model will fit in available VRAM, loading" model=/root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 library=cuda parallel=4 required="53.0 GiB" time=2024-11-01T00:40:14.555Z level=INFO source=server.go:105 msg="system memory" total="2267.4 GiB" free="2236.9 GiB" free_swap="8.0 GiB" time=2024-11-01T00:40:14.556Z level=INFO source=memory.go:326 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=81 layers.split=27,27,27 memory.available="[39.1 GiB 39.1 GiB 39.1 GiB]" memory.gpu_overhead="0 B" memory.required.full="53.0 GiB" memory.required.partial="53.0 GiB" memory.required.kv="2.5 GiB" memory.required.allocations="[18.6 GiB 17.1 GiB 17.3 GiB]" memory.weights.total="45.0 GiB" memory.weights.repeating="44.1 GiB" memory.weights.nonrepeating="974.6 MiB" memory.graph.full="1.3 GiB" memory.graph.partial="1.3 GiB" time=2024-11-01T00:40:14.557Z level=INFO source=server.go:388 msg="starting llama server" cmd="/usr/lib/ollama/runners/cuda_v12/ollama_llama_server --model /root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 --ctx-size 8192 --batch-size 512 --embedding --n-gpu-layers 81 --threads 96 --flash-attn --parallel 4 --tensor-split 27,27,27 --port 37747" time=2024-11-01T00:40:14.557Z level=INFO source=sched.go:449 msg="loaded runners" count=1 time=2024-11-01T00:40:14.557Z level=INFO source=server.go:587 msg="waiting for llama runner to start responding" time=2024-11-01T00:40:14.558Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server error" INFO [main] starting c++ runner | tid="140076011839488" timestamp=1730421614 INFO [main] build info | build=10 commit="b45ed63" tid="140076011839488" timestamp=1730421614 INFO [main] system info | n_threads=96 n_threads_batch=96 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140076011839488" timestamp=1730421614 total_threads=384 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="383" port="37747" tid="140076011839488" timestamp=1730421614 llama_model_loader: loaded meta data with 35 key-value pairs and 963 tensors from /root/.ollama/models/blobs/sha256-6e7fdda508e91cb0f63de5c15ff79ac63a1584ccafd751c07ca12b7f442101b8 (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 = qwen2 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen2.5 72B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen2.5 llama_model_loader: - kv 5: general.size_label str = 72B llama_model_loader: - kv 6: general.license str = other llama_model_loader: - kv 7: general.license.name str = qwen llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7... llama_model_loader: - kv 9: general.base_model.count u32 = 1 llama_model_loader: - kv 10: general.base_model.0.name str = Qwen2.5 72B llama_model_loader: - kv 11: general.base_model.0.organization str = Qwen llama_model_loader: - kv 12: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-72B llama_model_loader: - kv 13: general.tags arr[str,2] = ["chat", "text-generation"] llama_model_loader: - kv 14: general.languages arr[str,1] = ["en"] llama_model_loader: - kv 15: qwen2.block_count u32 = 80 llama_model_loader: - kv 16: qwen2.context_length u32 = 32768 llama_model_loader: - kv 17: qwen2.embedding_length u32 = 8192 llama_model_loader: - kv 18: qwen2.feed_forward_length u32 = 29568 llama_model_loader: - kv 19: qwen2.attention.head_count u32 = 64 llama_model_loader: - kv 20: qwen2.attention.head_count_kv u32 = 8 llama_model_loader: - kv 21: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 22: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 23: general.file_type u32 = 15 llama_model_loader: - kv 24: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 25: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 27: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 28: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 33: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 34: general.quantization_version u32 = 2 llama_model_loader: - type f32: 401 tensors llama_model_loader: - type q5_0: 40 tensors llama_model_loader: - type q8_0: 40 tensors llama_model_loader: - type q4_K: 401 tensors llama_model_loader: - type q5_K: 40 tensors llama_model_loader: - type q6_K: 41 tensors llm_load_vocab: special tokens cache size = 22 time=2024-11-01T00:40:14.809Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: token to piece cache size = 0.9310 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = qwen2 llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 152064 llm_load_print_meta: n_merges = 151387 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 32768 llm_load_print_meta: n_embd = 8192 llm_load_print_meta: n_layer = 80 llm_load_print_meta: n_head = 64 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_swa = 0 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 8 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 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 = 29568 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 = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 32768 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: ssm_dt_b_c_rms = 0 llm_load_print_meta: model type = 70B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 72.71 B llm_load_print_meta: model size = 44.15 GiB (5.22 BPW) llm_load_print_meta: general.name = Qwen2.5 72B Instruct llm_load_print_meta: BOS token = 151643 '<|endoftext|>' llm_load_print_meta: EOS token = 151645 '<|im_end|>' llm_load_print_meta: PAD token = 151643 '<|endoftext|>' llm_load_print_meta: LF token = 148848 'ÄĬ' llm_load_print_meta: EOT token = 151645 '<|im_end|>' llm_load_print_meta: EOG token = 151643 '<|endoftext|>' llm_load_print_meta: EOG token = 151645 '<|im_end|>' llm_load_print_meta: max token length = 256 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 3 CUDA devices: Device 0: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes Device 1: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes Device 2: NVIDIA A100-SXM4-40GB, compute capability 8.0, VMM: yes llm_load_tensors: ggml ctx size = 1.69 MiB time=2024-11-01T00:40:16.264Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server not responding" time=2024-11-01T00:40:17.417Z level=INFO source=server.go:621 msg="waiting for server to become available" status="llm server loading model" llm_load_tensors: offloading 80 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 81/81 layers to GPU llm_load_tensors: CPU buffer size = 668.25 MiB llm_load_tensors: CUDA0 buffer size = 14836.62 MiB llm_load_tensors: CUDA1 buffer size = 14310.50 MiB llm_load_tensors: CUDA2 buffer size = 15398.08 MiB llama_new_context_with_model: n_ctx = 8192 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 1 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 864.00 MiB llama_kv_cache_init: CUDA1 KV buffer size = 864.00 MiB llama_kv_cache_init: CUDA2 KV buffer size = 832.00 MiB llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 2.45 MiB llama_new_context_with_model: pipeline parallelism enabled (n_copies=4) llama_new_context_with_model: CUDA0 compute buffer size = 299.51 MiB llama_new_context_with_model: CUDA1 compute buffer size = 259.51 MiB llama_new_context_with_model: CUDA2 compute buffer size = 409.02 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 80.02 MiB llama_new_context_with_model: graph nodes = 2487 llama_new_context_with_model: graph splits = 4 INFO [main] model loaded | tid="140076011839488" timestamp=1730421622 time=2024-11-01T00:40:22.841Z level=INFO source=server.go:626 msg="llama runner started in 8.28 seconds" [GIN] 2024/11/01 - 00:40:22 | 200 | 9.618436111s | 127.0.0.1 | POST "/api/generate" [GIN] 2024/11/01 - 00:40:28 | 200 | 1.891222246s | 127.0.0.1 | POST "/api/chat" ![图片](https://github.com/user-attachments/assets/fbe2e829-f89b-49a0-8ab9-6c6b1dfe5082)
GiteaMirror commented 2026-05-09 14:05:14 -05:00
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@jmorganca commented on GitHub (Dec 29, 2024):

This should be fixed now, but let me know if that isn't the case and we can re-open.

<!-- gh-comment-id:2564828598 --> @jmorganca commented on GitHub (Dec 29, 2024): This should be fixed now, but let me know if that isn't the case and we can re-open.
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