Eval bug: llama-cpp-deepseek-r1.jinja template will miss the <think> tag #12107
Comments
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if remove |
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@Sherlock-Holo Thanks for filing this! FYI, this is currently halfway between working as intended (by the DeepSeek R1 template authors - both the latest official DS template and our |
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@ochafik did you mean use the official template or so before you add the streaming reasoning_content support, I should avoid use these template? |
@Sherlock-Holo Both, actually! Luckily the GGUF from bartowski was converted before DeepSeek updated their template to have the trailing
Probably best yes. Hope to share a draft PR for the streaming that detects and normalizes pre-fed think tags this weekend 🤞 |
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 goneFirst 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" }, "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": "1" }, "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": "。" }, "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": "如" }, "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": "您" }, "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": "有任何" }, "finish_reason": null, "index": 0 } ], "created": 1740734647, "id": 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