Closed
Description
Name and Version
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: Tesla T4, compute capability 7.5, VMM: yes
version: 4790 (438a839)
Operating systems
Linux
GGML backends
CUDA
Hardware
tesla t4
Models
gguf deepseek-r1:14b, downloaded from ollama
Problem description & steps to reproduce
/root/git/llama.cpp/build/bin/llama-server \
-m /usr/share/ollama/.ollama/models/blobs/sha256-6e9f90f02bb3b39b59e81916e8cfce9deb45aeaeb9a54a5be4414486b907dc1e \
-ngl 99 \
--temp 0.6 \
--no-webui \
--host 0.0.0.0 \
--port 20004 \
--jinja \
-fa \
--chat-template-file /root/git/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinjathis will use llama-cpp-deepseek-r1.jinja as the template, however, when using stream mode, the output content will miss the start <think> tag, but the </think> still exists, if remove the flag --chat-template-file /root/git/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinja, problem gone
First Bad Commit
No response
Relevant log output
/root/git/llama.cpp/build/bin/llama-server \
-m /usr/share/ollama/.ollama/models/blobs/sha256-6e9f90f02bb3b39b59e81916e8cfce9deb45aeaeb9a54a5be4414486b907dc1e \
-ngl 99 \
--temp 0.6 \
--no-webui \
--host 0.0.0.0 \
--port 20004 \
--jinja \
-fa \
--chat-template-file /root/git/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinja
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: Tesla T4, compute capability 7.5, VMM: yes
system info: n_threads = 20, n_threads_batch = 20, total_threads = 20
system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 750 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
Web UI is disabled
main: HTTP server is listening, hostname: 0.0.0.0, port: 20004, http threads: 19
main: loading model
srv load_model: loading model '/usr/share/ollama/.ollama/models/blobs/sha256-6e9f90f02bb3b39b59e81916e8cfce9deb45aeaeb9a54a5be4414486b907dc1e'
llama_model_load_from_file_impl: using device CUDA0 (Tesla T4) - 14814 MiB free
llama_model_loader: loaded meta data with 26 key-value pairs and 579 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-6e9f90f02bb3b39b59e81916e8cfce9deb45aeaeb9a54a5be4414486b907dc1e (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 = DeepSeek R1 Distill Qwen 14B
llama_model_loader: - kv 3: general.basename str = DeepSeek-R1-Distill-Qwen
llama_model_loader: - kv 4: general.size_label str = 14B
llama_model_loader: - kv 5: qwen2.block_count u32 = 48
llama_model_loader: - kv 6: qwen2.context_length u32 = 131072
llama_model_loader: - kv 7: qwen2.embedding_length u32 = 5120
llama_model_loader: - kv 8: qwen2.feed_forward_length u32 = 13824
llama_model_loader: - kv 9: qwen2.attention.head_count u32 = 40
llama_model_loader: - kv 10: qwen2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 11: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 12: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 13: general.file_type u32 = 15
llama_model_loader: - kv 14: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 15: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 151646
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 241 tensors
llama_model_loader: - type q4_K: 289 tensors
llama_model_loader: - type q6_K: 49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 8.37 GiB (4.87 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 5120
print_info: n_layer = 48
print_info: n_head = 40
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 5
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-05
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: n_ff = 13824
print_info: n_expert = 0
print_info: n_expert_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 = 131072
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 14B
print_info: model params = 14.77 B
print_info: general.name = DeepSeek R1 Distill Qwen 14B
print_info: vocab type = BPE
print_info: n_vocab = 152064
print_info: n_merges = 151387
print_info: BOS token = 151646 '<｜begin▁of▁sentence｜>'
print_info: EOS token = 151643 '<｜end▁of▁sentence｜>'
print_info: EOT token = 151643 '<｜end▁of▁sentence｜>'
print_info: PAD token = 151643 '<｜end▁of▁sentence｜>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<｜end▁of▁sentence｜>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 48 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 49/49 layers to GPU
load_tensors: CPU_Mapped model buffer size = 417.66 MiB
load_tensors: CUDA0 model buffer size = 8148.38 MiB
...........................................................................................
llama_init_from_model: n_seq_max = 1
llama_init_from_model: n_ctx = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch = 2048
llama_init_from_model: n_ubatch = 512
llama_init_from_model: flash_attn = 1
llama_init_from_model: freq_base = 1000000.0
llama_init_from_model: freq_scale = 1
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 48, can_shift = 1
llama_kv_cache_init: CUDA0 KV buffer size = 768.00 MiB
llama_init_from_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB
llama_init_from_model: CUDA_Host output buffer size = 0.58 MiB
llama_init_from_model: CUDA0 compute buffer size = 307.00 MiB
llama_init_from_model: CUDA_Host compute buffer size = 18.01 MiB
llama_init_from_model: graph nodes = 1495
llama_init_from_model: graph splits = 2
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)
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 4096
main: model loaded
main: chat template, chat_template: {%- if not add_generation_prompt is defined -%}
{%- set add_generation_prompt = false -%}
{%- endif -%}
{%- set ns = namespace(is_first=false, is_tool_outputs=false, is_output_first=true, system_prompt='') -%}
{%- for message in messages -%}
{%- if message['role'] == 'system' -%}
{%- set ns.system_prompt = message['content'] -%}
{%- endif -%}
{%- endfor -%}
{{bos_token}}
{%- if tools %}
You can call any of the following function tools to satisfy the user's requests: {{tools | map(attribute='function') | tojson(indent=2)}}
Example function tool call syntax:
<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>example_function_name
{
"arg1": "some_value"
...
}
<｜tool▁call▁end｜><｜tool▁calls▁end｜>
{% endif -%}
{{ns.system_prompt}}
{%- macro flush_tool_outputs() -%}
{%- if ns.is_tool_outputs -%}
{{- '<｜tool▁outputs▁end｜><｜end▁of▁sentence｜>' -}}
{%- set ns.is_tool_outputs = false -%}
{%- endif -%}
{%- endmacro -%}
{{- flush_tool_outputs() -}}
{%- for message in messages -%}
{%- if message['role'] != 'tool' -%}
{{- flush_tool_outputs() -}}
{%- endif -%}
{%- if message['role'] == 'user' -%}
{{- '<｜User｜>' + message['content'] + '<｜end▁of▁sentence｜>' -}}
{%- endif -%}
{%- if message['role'] == 'assistant' and message['content'] is none -%}
{{- '<｜Assistant｜><｜tool▁calls▁begin｜>' -}}
{%- set ns.is_first = true -%}
{%- for tc in message['tool_calls'] -%}
{%- if ns.is_first -%}
{%- set ns.is_first = false -%}
{%- else -%}
{{- '\n' -}}
{%- endif -%}
{%- set tool_name = tc['function']['name'] -%}
{%- set tool_args = tc['function']['arguments'] -%}
{{- '<｜tool▁call▁begin｜>' + tc['type'] + '<｜tool▁sep｜>' + tool_name + '\n' + '' + '\n' + tool_args + '\n' + '' + '<｜tool▁call▁end｜>' -}}
{%- endfor -%}
{{- '<｜tool▁calls▁end｜><｜end▁of▁sentence｜>' -}}
{%- endif -%}
{%- if message['role'] == 'assistant' and message['content'] is not none -%}
{{- flush_tool_outputs() -}}
{%- set content = message['content'] -%}
{%- if '</think>' in content -%}
{%- set content = content.split('</think>')[-1] -%}
{%- endif -%}
{{- '<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>' -}}
{%- endif -%}
{%- if message['role'] == 'tool' -%}
{%- set ns.is_tool_outputs = true -%}
{%- if ns.is_output_first -%}
{{- '<｜tool▁outputs▁begin｜>' -}}
{%- set ns.is_output_first = false -%}
{%- endif -%}
{{- '\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>' -}}
{%- endif -%}
{%- endfor -%}
{{- flush_tool_outputs() -}}
{%- if add_generation_prompt and not ns.is_tool_outputs -%}
{{- '<｜Assistant｜><think>\n' -}}
{%- endif -%}, example_format: 'You are a helpful assistant<｜User｜>Hello<｜end▁of▁sentence｜><｜Assistant｜>Hi there<｜end▁of▁sentence｜><｜User｜>How are you?<｜end▁of▁sentence｜><｜Assistant｜><think>
'
main: server is listening on http://0.0.0.0:20004 - starting the main loop
srv update_slots: all slots are idle
srv params_from_: Chat format: DeepSeek R1 (extract reasoning)
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 12
slot update_slots: id 0 | task 0 | kv cache rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 12, n_tokens = 12, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_past = 12, n_tokens = 12
slot release: id 0 | task 0 | stop processing: n_past = 84, truncated = 0
slot print_timing: id 0 | task 0 |
prompt eval time = 128.41 ms / 12 tokens ( 10.70 ms per token, 93.45 tokens per second)
eval time = 3607.62 ms / 73 tokens ( 49.42 ms per token, 20.23 tokens per second)
total time = 3736.03 ms / 85 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 10.30.200.19 200
use http POST http://gpu-dev:20004/v1/chat/completions \
model=deepseek-r1-14b-cpp \
messages:='[{"role": "user", "content": "非常详细地介绍一下你自己?"}]' \
stream:=true
to send request, and get
HTTP/1.1 200 OK
Access-Control-Allow-Origin:
Content-Type: text/event-stream
Keep-Alive: timeout=5, max=100
Server: llama.cpp
Transfer-Encoding: chunked
data: {
"choices": [
{
"delta": {
"content": "您好"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "！"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "我是"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "由"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "中国的"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "深度"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "求"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "索"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "（"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "Deep"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "Seek"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "）"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "公司"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734646,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "开发"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "的"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "智能"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "助手"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "Deep"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "Seek"
},
"finish_reason": null,
"index": 0
}
],
"created": 1740734647,
"id": "chatcmpl-huCXGDWAI250H1Sid6SfwT2F6QcrjN8p",
"model": "deepseek-r1-14b-cpp",
"object": "chat.completion.chunk",
"system_fingerprint": "b4790-438a8392"
}
data: {
"choices": [
{
"delta": {
"content": "-R"
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data: [DONE]