github-starred/ollama
2
0
Fork 0
mirror of https://github.com/ollama/ollama.git synced 2026-05-07 08:30:05 -05:00
Code Issues 14.5k Packages Projects Releases 120 Wiki Activity

[GH-ISSUE #6601] when i try to visit https://xxxxxxxx.com/api/chat，it is very slow #29918

New Issue
Closed
opened 2026-04-22 09:16:07 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-04-22 09:16:07 -05:00
Owner
Copy Link

Originally created by @lessuit on GitHub (Sep 3, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/6601

Originally assigned to: @dhiltgen on GitHub.

What is the issue?

there is docker logs:

time=2024-09-03T06:16:48.144Z level=INFO source=sched.go:715 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb gpu=GPU-1a66ac9e-e1b3-db2b-4e52-f54d2f373819 parallel=4 available=84100120576 required="42.2 GiB"
time=2024-09-03T06:16:48.150Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=81 layers.split="" memory.available="[78.3 GiB]" memory.required.full="42.2 GiB" memory.required.partial="42.2 GiB" memory.required.kv="2.5 GiB" memory.required.allocations="[42.2 GiB]" memory.weights.total="39.3 GiB" memory.weights.repeating="38.3 GiB" memory.weights.nonrepeating="974.6 MiB" memory.graph.full="1.0 GiB" memory.graph.partial="1.3 GiB"
time=2024-09-03T06:16:48.163Z level=INFO source=server.go:391 msg="starting llama server" cmd="/tmp/ollama3339020570/runners/cuda_v12/ollama_llama_server --model /root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 81 --parallel 4 --port 46231"
time=2024-09-03T06:16:48.164Z level=INFO source=sched.go:450 msg="loaded runners" count=1
time=2024-09-03T06:16:48.164Z level=INFO source=server.go:591 msg="waiting for llama runner to start responding"
time=2024-09-03T06:16:48.164Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="1e6f655" tid="139860331409408" timestamp=1725344208
INFO [main] system info | n_threads=48 n_threads_batch=-1 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 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139860331409408" timestamp=1725344208 total_threads=96
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="95" port="46231" tid="139860331409408" timestamp=1725344208
llama_model_loader: loaded meta data with 21 key-value pairs and 963 tensors from /root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb (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.name str = Qwen2-72B-Instruct
llama_model_loader: - kv 2: qwen2.block_count u32 = 80
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 8192
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 29568
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 64
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,152064] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 20: general.quantization_version u32 = 2
llama_model_loader: - type f32: 401 tensors
llama_model_loader: - type q4_0: 561 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-09-03T06:16:48.416Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 421
llm_load_vocab: token to piece cache size = 0.9352 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: model type = 70B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 72.71 B
llm_load_print_meta: model size = 38.39 GiB (4.54 BPW)
llm_load_print_meta: general.name = Qwen2-72B-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: max token length = 256
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA A100-SXM4-80GB, compute capability 8.0, VMM: yes
llm_load_tensors: ggml ctx size = 0.85 MiB
time=2024-09-03T06:16:49.873Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding"
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 = 38647.70 MiB
time=2024-09-03T06:16:51.930Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
time=2024-09-03T06:16:57.155Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding"
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 = 0
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 = 2560.00 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: CUDA0 compute buffer size = 1104.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 32.01 MiB
llama_new_context_with_model: graph nodes = 2806
llama_new_context_with_model: graph splits = 2
time=2024-09-03T06:16:58.760Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
INFO [main] model loaded | tid="139860331409408" timestamp=1725344218
time=2024-09-03T06:16:59.013Z level=INFO source=server.go:630 msg="llama runner started in 10.85 seconds"
[GIN] 2024/09/03 - 06:17:08 | 200 | 20.303390852s | 113.57.107.43 | POST "/api/chat"

OS

Linux, Docker

GPU

Nvidia

CPU

Intel

Ollama version

0.3.8 and 0.3.9

Originally created by @lessuit on GitHub (Sep 3, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/6601 Originally assigned to: @dhiltgen on GitHub. ### What is the issue? there is docker logs: time=2024-09-03T06:16:48.144Z level=INFO source=sched.go:715 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb gpu=GPU-1a66ac9e-e1b3-db2b-4e52-f54d2f373819 parallel=4 available=84100120576 required="42.2 GiB" time=2024-09-03T06:16:48.150Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=81 layers.split="" memory.available="[78.3 GiB]" memory.required.full="42.2 GiB" memory.required.partial="42.2 GiB" memory.required.kv="2.5 GiB" memory.required.allocations="[42.2 GiB]" memory.weights.total="39.3 GiB" memory.weights.repeating="38.3 GiB" memory.weights.nonrepeating="974.6 MiB" memory.graph.full="1.0 GiB" memory.graph.partial="1.3 GiB" time=2024-09-03T06:16:48.163Z level=INFO source=server.go:391 msg="starting llama server" cmd="/tmp/ollama3339020570/runners/cuda_v12/ollama_llama_server --model /root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 81 --parallel 4 --port 46231" time=2024-09-03T06:16:48.164Z level=INFO source=sched.go:450 msg="loaded runners" count=1 time=2024-09-03T06:16:48.164Z level=INFO source=server.go:591 msg="waiting for llama runner to start responding" time=2024-09-03T06:16:48.164Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="1e6f655" tid="139860331409408" timestamp=1725344208 INFO [main] system info | n_threads=48 n_threads_batch=-1 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 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139860331409408" timestamp=1725344208 total_threads=96 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="95" port="46231" tid="139860331409408" timestamp=1725344208 llama_model_loader: loaded meta data with 21 key-value pairs and 963 tensors from /root/.ollama/models/blobs/sha256-f6ac28d6f58ae1522734d1df834e6166e0813bb1919e86aafb4c0551eb4ce2bb (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.name str = Qwen2-72B-Instruct llama_model_loader: - kv 2: qwen2.block_count u32 = 80 llama_model_loader: - kv 3: qwen2.context_length u32 = 32768 llama_model_loader: - kv 4: qwen2.embedding_length u32 = 8192 llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 29568 llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 64 llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: general.file_type u32 = 2 llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo... llama_model_loader: - kv 20: general.quantization_version u32 = 2 llama_model_loader: - type f32: 401 tensors llama_model_loader: - type q4_0: 561 tensors llama_model_loader: - type q6_K: 1 tensors time=2024-09-03T06:16:48.416Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special tokens cache size = 421 llm_load_vocab: token to piece cache size = 0.9352 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: model type = 70B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 72.71 B llm_load_print_meta: model size = 38.39 GiB (4.54 BPW) llm_load_print_meta: general.name = Qwen2-72B-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: max token length = 256 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA A100-SXM4-80GB, compute capability 8.0, VMM: yes llm_load_tensors: ggml ctx size = 0.85 MiB time=2024-09-03T06:16:49.873Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding" 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 = 38647.70 MiB time=2024-09-03T06:16:51.930Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model" time=2024-09-03T06:16:57.155Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding" 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 = 0 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 = 2560.00 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: CUDA0 compute buffer size = 1104.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 32.01 MiB llama_new_context_with_model: graph nodes = 2806 llama_new_context_with_model: graph splits = 2 time=2024-09-03T06:16:58.760Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model" INFO [main] model loaded | tid="139860331409408" timestamp=1725344218 time=2024-09-03T06:16:59.013Z level=INFO source=server.go:630 msg="llama runner started in 10.85 seconds" [GIN] 2024/09/03 - 06:17:08 | 200 | 20.303390852s | 113.57.107.43 | POST "/api/chat" ### OS Linux, Docker ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.8 and 0.3.9
GiteaMirror added the needs more info label 2026-04-22 09:16:07 -05:00
GiteaMirror commented 2026-04-22 09:16:10 -05:00
Author
Owner
Copy Link

@rick-github commented on GitHub (Sep 3, 2024):

10 seconds to load a 40G model doesn't seem unreasonable. Another user found that a large model was slow to load (https://github.com/ollama/ollama/issues/6425#issuecomment-2316002395) and successfully reduced load time with the following command. Not sure it will help in your case but can't hurt to try.

echo 0 > /proc/sys/kernel/numa_balancing
<!-- gh-comment-id:2327275529 --> @rick-github commented on GitHub (Sep 3, 2024): 10 seconds to load a 40G model doesn't seem unreasonable. Another user found that a large model was slow to load (https://github.com/ollama/ollama/issues/6425#issuecomment-2316002395) and successfully reduced load time with the following command. Not sure it will help in your case but can't hurt to try. ``` echo 0 > /proc/sys/kernel/numa_balancing ```
GiteaMirror commented 2026-04-22 09:16:12 -05:00
Author
Owner
Copy Link

@dhiltgen commented on GitHub (Sep 3, 2024):

10s does seem reasonable for a load time of a large model.

Can you elaborate what "very slow" means? Are you just referring to latency, or is the token rate particularly low? You can try using the CLI with --verbose to get more timing information, including load duration. (e.g. ollama run --verbose llama3.1:70b hello)

<!-- gh-comment-id:2327384719 --> @dhiltgen commented on GitHub (Sep 3, 2024): 10s does seem reasonable for a load time of a large model. Can you elaborate what "very slow" means? Are you just referring to latency, or is the token rate particularly low? You can try using the CLI with `--verbose` to get more timing information, including load duration. (e.g. `ollama run --verbose llama3.1:70b hello`)
GiteaMirror commented 2026-04-22 09:16:13 -05:00
Author
Owner
Copy Link

@lessuit commented on GitHub (Sep 4, 2024):

10 seconds to load a 40G model doesn't seem unreasonable. Another user found that a large model was slow to load (#6425 (comment)) and successfully reduced load time with the following command. Not sure it will help in your case but can't hurt to try.

echo 0 > /proc/sys/kernel/numa_balancing

okey i get it， thanks for your reply and i will close the issue

<!-- gh-comment-id:2327717300 --> @lessuit commented on GitHub (Sep 4, 2024): > 10 seconds to load a 40G model doesn't seem unreasonable. Another user found that a large model was slow to load ([#6425 (comment)](https://github.com/ollama/ollama/issues/6425#issuecomment-2316002395)) and successfully reduced load time with the following command. Not sure it will help in your case but can't hurt to try. > > ``` > echo 0 > /proc/sys/kernel/numa_balancing > ``` okey i get it， thanks for your reply and i will close the issue
Sign in to join this conversation.
No Branch/Tag Specified
Branches Tags
Labels
Clear labels
amd
api
app
bug
build
cli
cloud
compatibility
context-length
create
docker
documentation
embeddings
feature request
feedback wanted
good first issue
gpt-oss
gpu
harmony
help wanted
image
install
intel
js
launch
linux
macos
memory
mlx
model
needs more info
networking
nvidia
ollama.com
performance
pull-request
Mirrored from GitHub Pull Request
 python
question
registry
rendering
thinking
tools
top
vulkan
windows
wsl
No Label needs more info
Milestone
No items
No Milestone
Projects
Clear projects
No project
Assignees
Clear assignees
GiteaMirror ninjasurge
No Assignees
1 Participants
Notifications
Due Date
No due date set.
Dependencies

No dependencies set.

Reference: github-starred/ollama#29918
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?