Reasoning Parser#
SGLang supports parsing reasoning content out from “normal” content for reasoning models such as DeepSeek R1.
Supported Models & Parsers#
Model |
Reasoning tags |
Parser |
Notes |
|---|---|---|---|
|
|
Supports all variants (R1, R1-0528, R1-Distill) |
|
|
|
Including
DeepSeek‑V3.2.
Supports |
|
|
|
Supports |
|
|
|
Always generates thinking content |
|
|
|
Uses special thinking delimiters. Also requires
|
|
|
|
N/A |
Model-Specific Behaviors#
DeepSeek-R1 Family:
DeepSeek-R1: No
<think>start tag, jumps directly to thinking contentDeepSeek-R1-0528: Generates both
<think>start and</think>end tagsBoth are handled by the same
deepseek-r1parser
DeepSeek-V3 Family:
DeepSeek-V3.1/V3.2: Hybrid model supporting both thinking and non-thinking modes, use the
deepseek-v3parser andthinkingparameter (NOTE: notenable_thinking)
Qwen3 Family:
Standard Qwen3 (e.g., Qwen3-2507): Use
qwen3parser, supportsenable_thinkingin chat templatesQwen3-Thinking (e.g., Qwen3-235B-A22B-Thinking-2507): Use
qwen3orqwen3-thinkingparser, always thinks
Kimi K2:
Kimi K2 Thinking: Uses special
◁think▷and◁/think▷tags. For agentic tool use, also specify--tool-call-parser kimi_k2.
GPT OSS:
GPT OSS: Uses special
<|channel|>analysis<|message|>and<|end|>tags
Usage#
Launching the Server#
Specify the --reasoning-parser option.
[1]:
import requests
from openai import OpenAI
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process
server_process, port = launch_server_cmd(
"python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --host 0.0.0.0 --reasoning-parser deepseek-r1 --log-level warning"
)
wait_for_server(f"http://localhost:{port}", process=server_process)
[2026-02-27 22:02:12] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-02-27 22:02:12] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-02-27 22:02:12] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-02-27 22:02:18] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-02-27 22:02:18] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-02-27 22:02:18] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-02-27 22:02:21] INFO server_args.py:1854: Attention backend not specified. Use fa3 backend by default.
[2026-02-27 22:02:21] INFO server_args.py:2924: Set soft_watchdog_timeout since in CI
[2026-02-27 22:02:29] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-02-27 22:02:29] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-02-27 22:02:29] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-02-27 22:02:29] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-02-27 22:02:29] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-02-27 22:02:29] INFO utils.py:164: NumExpr defaulting to 16 threads.
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[2026-02-27 22:02:35] Ignore import error when loading sglang.srt.models.glm_ocr: No module named 'transformers.models.glm_ocr'
[2026-02-27 22:02:35] Ignore import error when loading sglang.srt.models.glm_ocr_nextn: No module named 'transformers.models.glm_ocr'
[2026-02-27 22:02:35] Ignore import error when loading sglang.srt.models.glmasr: cannot import name 'GlmAsrConfig' from 'transformers' (/usr/local/lib/python3.10/dist-packages/transformers/__init__.py)
Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 50% Completed | 1/2 [00:05<00:05, 5.51s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:09<00:00, 4.89s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:09<00:00, 4.98s/it]
Capturing batches (bs=1 avail_mem=31.27 GB): 100%|██████████| 3/3 [00:00<00:00, 11.17it/s]
/usr/local/lib/python3.10/dist-packages/fastapi/routing.py:105: FastAPIDeprecationWarning: ORJSONResponse is deprecated, FastAPI now serializes data directly to JSON bytes via Pydantic when a return type or response model is set, which is faster and doesn't need a custom response class. Read more in the FastAPI docs: https://fastapi.tiangolo.com/advanced/custom-response/#orjson-or-response-model and https://fastapi.tiangolo.com/tutorial/response-model/
response = await f(request)
NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
To reduce the log length, we set the log level to warning for the server, the default log level is info.
We are running those notebooks in a CI environment, so the throughput is not representative of the actual performance.
Note that --reasoning-parser defines the parser used to interpret responses.
OpenAI Compatible API#
Using the OpenAI compatible API, the contract follows the DeepSeek API design established with the release of DeepSeek-R1:
reasoning_content: The content of the CoT.content: The content of the final answer.
[2]:
# Initialize OpenAI-like client
client = OpenAI(api_key="None", base_url=f"http://0.0.0.0:{port}/v1")
model_name = client.models.list().data[0].id
messages = [
{
"role": "user",
"content": "What is 1+3?",
}
]
Non-Streaming Request#
[3]:
response_non_stream = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.6,
top_p=0.95,
stream=False, # Non-streaming
extra_body={"separate_reasoning": True},
)
print_highlight("==== Reasoning ====")
print_highlight(response_non_stream.choices[0].message.reasoning_content)
print_highlight("==== Text ====")
print_highlight(response_non_stream.choices[0].message.content)
==== Reasoning ====
I start by identifying the two numbers in the problem: 1 and 3.
Next, I add these numbers together: 1 plus 3 equals 4.
Therefore, the final answer is 4.
Next, I add these numbers together: 1 plus 3 equals 4.
Therefore, the final answer is 4.
==== Text ====
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
**Answer:** \(\boxed{4}\)
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
**Answer:** \(\boxed{4}\)
Streaming Request#
[4]:
response_stream = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.6,
top_p=0.95,
stream=True, # Non-streaming
extra_body={"separate_reasoning": True},
)
reasoning_content = ""
content = ""
for chunk in response_stream:
if chunk.choices[0].delta.content:
content += chunk.choices[0].delta.content
if chunk.choices[0].delta.reasoning_content:
reasoning_content += chunk.choices[0].delta.reasoning_content
print_highlight("==== Reasoning ====")
print_highlight(reasoning_content)
print_highlight("==== Text ====")
print_highlight(content)
==== Reasoning ====
To solve the problem \(1 + 3\), I start by identifying the two numbers involved, which are 1 and 3.
Next, I add these two numbers together.
Finally, the sum of 1 and 3 is 4.
Next, I add these two numbers together.
Finally, the sum of 1 and 3 is 4.
==== Text ====
**Solution:**
To find the sum of \(1 + 3\), follow these simple steps:
1. **Start with the first number:**
\[
1
\]
2. **Add the second number to it:**
\[
1 + 3
\]
3. **Calculate the total:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
Optionally, you can buffer the reasoning content to the last reasoning chunk (or the first chunk after the reasoning content).
[5]:
response_stream = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.6,
top_p=0.95,
stream=True, # Non-streaming
extra_body={"separate_reasoning": True, "stream_reasoning": False},
)
reasoning_content = ""
content = ""
for chunk in response_stream:
if chunk.choices[0].delta.content:
content += chunk.choices[0].delta.content
if chunk.choices[0].delta.reasoning_content:
reasoning_content += chunk.choices[0].delta.reasoning_content
print_highlight("==== Reasoning ====")
print_highlight(reasoning_content)
print_highlight("==== Text ====")
print_highlight(content)
==== Reasoning ====
First, I identify the two numbers that need to be added: 1 and 3.
Next, I perform the addition of these two numbers.
Finally, I calculate the sum to find that 1 plus 3 equals 4.
Next, I perform the addition of these two numbers.
Finally, I calculate the sum to find that 1 plus 3 equals 4.
==== Text ====
**Solution:**
To find the sum of \(1\) and \(3\), follow these simple steps:
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Perform the addition:**
\[
1 + 3 = 4
\]
3. **Final Answer:**
\[
\boxed{4}
\]
The reasoning separation is enable by default when specify . To disable it, set the ``separate_reasoning`` option to ``False`` in request.
[6]:
response_non_stream = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.6,
top_p=0.95,
stream=False, # Non-streaming
extra_body={"separate_reasoning": False},
)
print_highlight("==== Original Output ====")
print_highlight(response_non_stream.choices[0].message.content)
==== Original Output ====
I need to add the numbers 1 and 3 together.
First, I identify the two numbers involved in the addition: 1 and 3.
Next, I perform the addition operation by combining these two numbers.
Finally, I calculate that 1 plus 3 equals 4.
**Solution:**
We are asked to find the sum of \(1\) and \(3\).
\[
1 + 3 = 4
\]
Therefore, the final answer is \(\boxed{4}\).
First, I identify the two numbers involved in the addition: 1 and 3.
Next, I perform the addition operation by combining these two numbers.
Finally, I calculate that 1 plus 3 equals 4.
**Solution:**
We are asked to find the sum of \(1\) and \(3\).
\[
1 + 3 = 4
\]
Therefore, the final answer is \(\boxed{4}\).
SGLang Native API#
[7]:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
input = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, return_dict=False
)
gen_url = f"http://localhost:{port}/generate"
gen_data = {
"text": input,
"sampling_params": {
"skip_special_tokens": False,
"max_new_tokens": 1024,
"temperature": 0.6,
"top_p": 0.95,
},
}
gen_response = requests.post(gen_url, json=gen_data).json()["text"]
print_highlight("==== Original Output ====")
print_highlight(gen_response)
parse_url = f"http://localhost:{port}/separate_reasoning"
separate_reasoning_data = {
"text": gen_response,
"reasoning_parser": "deepseek-r1",
}
separate_reasoning_response_json = requests.post(
parse_url, json=separate_reasoning_data
).json()
print_highlight("==== Reasoning ====")
print_highlight(separate_reasoning_response_json["reasoning_text"])
print_highlight("==== Text ====")
print_highlight(separate_reasoning_response_json["text"])
==== Original Output ====
First, I recognize that the problem is asking for the sum of 1 and 3.
Next, I add the two numbers together.
Finally, I arrive at the total, which is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = \boxed{4}
\]
Next, I add the two numbers together.
Finally, I arrive at the total, which is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = \boxed{4}
\]
/usr/local/lib/python3.10/dist-packages/fastapi/routing.py:313: FastAPIDeprecationWarning: ORJSONResponse is deprecated, FastAPI now serializes data directly to JSON bytes via Pydantic when a return type or response model is set, which is faster and doesn't need a custom response class. Read more in the FastAPI docs: https://fastapi.tiangolo.com/advanced/custom-response/#orjson-or-response-model and https://fastapi.tiangolo.com/tutorial/response-model/
return await dependant.call(**values)
==== Reasoning ====
First, I recognize that the problem is asking for the sum of 1 and 3.
Next, I add the two numbers together.
Finally, I arrive at the total, which is 4.
Next, I add the two numbers together.
Finally, I arrive at the total, which is 4.
==== Text ====
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = \boxed{4}
\]
We are asked to find the sum of 1 and 3.
\[
1 + 3 = \boxed{4}
\]
[8]:
terminate_process(server_process)
Offline Engine API#
[9]:
import sglang as sgl
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.utils import print_highlight
llm = sgl.Engine(model_path="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
input = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, return_dict=False
)
sampling_params = {
"max_new_tokens": 1024,
"skip_special_tokens": False,
"temperature": 0.6,
"top_p": 0.95,
}
result = llm.generate(prompt=input, sampling_params=sampling_params)
generated_text = result["text"] # Assume there is only one prompt
print_highlight("==== Original Output ====")
print_highlight(generated_text)
parser = ReasoningParser("deepseek-r1")
reasoning_text, text = parser.parse_non_stream(generated_text)
print_highlight("==== Reasoning ====")
print_highlight(reasoning_text)
print_highlight("==== Text ====")
print_highlight(text)
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
[2026-02-27 22:03:00] INFO server_args.py:1854: Attention backend not specified. Use fa3 backend by default.
[2026-02-27 22:03:00] INFO server_args.py:2924: Set soft_watchdog_timeout since in CI
[2026-02-27 22:03:00] INFO engine.py:157: server_args=ServerArgs(model_path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', tokenizer_path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=False, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='127.0.0.1', port=30000, fastapi_root_path='', grpc_mode=False, skip_server_warmup=False, warmups=None, nccl_port=None, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, rl_quant_profile=None, mem_fraction_static=0.835, max_running_requests=128, max_queued_requests=None, max_total_tokens=20480, chunked_prefill_size=8192, enable_dynamic_chunking=False, max_prefill_tokens=16384, prefill_max_requests=None, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=1, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', enable_prefill_delayer=False, prefill_delayer_max_delay_passes=30, prefill_delayer_token_usage_low_watermark=None, prefill_delayer_forward_passes_buckets=None, prefill_delayer_wait_seconds_buckets=None, device='cuda', tp_size=1, pp_size=1, pp_max_micro_batch_size=None, pp_async_batch_depth=0, stream_interval=1, stream_output=False, random_seed=39759744, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, soft_watchdog_timeout=300, dist_timeout=None, download_dir=None, model_checksum=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, custom_sigquit_handler=None, log_level='error', log_level_http=None, log_requests=False, log_requests_level=2, log_requests_format='text', log_requests_target=None, uvicorn_access_log_exclude_prefixes=[], crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, extra_metric_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=40, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, otlp_traces_endpoint='localhost:4317', export_metrics_to_file=False, export_metrics_to_file_dir=None, api_key=None, admin_api_key=None, served_model_name='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', weight_version='default', chat_template=None, hf_chat_template_name=None, completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', attn_cp_size=1, moe_dp_size=1, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, enable_lora_overlap_loading=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='csgmv', max_lora_chunk_size=16, attention_backend='fa3', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', mm_attention_backend=None, fp8_gemm_runner_backend='auto', fp4_gemm_runner_backend='flashinfer_cutlass', nsa_prefill_backend=None, nsa_decode_backend=None, disable_flashinfer_autotune=False, mamba_backend='triton', speculative_algorithm=None, speculative_draft_model_path=None, speculative_draft_model_revision=None, speculative_draft_load_format=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, speculative_attention_mode='prefill', speculative_draft_attention_backend=None, speculative_moe_runner_backend='auto', speculative_moe_a2a_backend=None, speculative_draft_model_quantization=None, speculative_ngram_min_match_window_size=1, speculative_ngram_max_match_window_size=12, speculative_ngram_min_bfs_breadth=1, speculative_ngram_max_bfs_breadth=10, speculative_ngram_match_type='BFS', speculative_ngram_branch_length=18, speculative_ngram_capacity=10000000, enable_multi_layer_eagle=False, ep_size=1, moe_a2a_backend='none', moe_runner_backend='auto', flashinfer_mxfp4_moe_precision='default', enable_flashinfer_allreduce_fusion=False, enable_aiter_allreduce_fusion=False, deepep_mode='auto', ep_num_redundant_experts=0, ep_dispatch_algorithm=None, init_expert_location='trivial', enable_eplb=False, eplb_algorithm='auto', eplb_rebalance_num_iterations=1000, eplb_rebalance_layers_per_chunk=None, eplb_min_rebalancing_utilization_threshold=1.0, expert_distribution_recorder_mode=None, expert_distribution_recorder_buffer_size=1000, enable_expert_distribution_metrics=False, deepep_config=None, moe_dense_tp_size=None, elastic_ep_backend=None, enable_elastic_expert_backup=False, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype=None, mamba_full_memory_ratio=0.9, mamba_scheduler_strategy='no_buffer', mamba_track_interval=256, linear_attn_backend='triton', linear_attn_decode_backend=None, linear_attn_prefill_backend=None, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through', hicache_io_backend='kernel', hicache_mem_layout='layer_first', disable_hicache_numa_detect=False, hicache_storage_backend=None, hicache_storage_prefetch_policy='best_effort', hicache_storage_backend_extra_config=None, hierarchical_sparse_attention_extra_config=None, enable_lmcache=False, kt_weight_path=None, kt_method=None, kt_cpuinfer=None, kt_threadpool_count=None, kt_num_gpu_experts=None, kt_max_deferred_experts_per_token=None, dllm_algorithm=None, dllm_algorithm_config=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, cpu_offload_gb=0, offload_group_size=-1, offload_num_in_group=1, offload_prefetch_step=1, offload_mode='cpu', multi_item_scoring_delimiter=None, disable_radix_cache=False, cuda_graph_max_bs=4, cuda_graph_bs=[1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_profile_cuda_graph=False, enable_cudagraph_gc=False, enable_layerwise_nvtx_marker=False, enable_nccl_nvls=False, enable_symm_mem=False, disable_flashinfer_cutlass_moe_fp4_allgather=False, enable_tokenizer_batch_encode=False, disable_tokenizer_batch_decode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, enable_mscclpp=False, enable_torch_symm_mem=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_two_batch_overlap=False, enable_single_batch_overlap=False, tbo_token_distribution_threshold=0.48, enable_torch_compile=False, enable_piecewise_cuda_graph=False, enable_torch_compile_debug_mode=False, torch_compile_max_bs=32, piecewise_cuda_graph_max_tokens=8192, piecewise_cuda_graph_tokens=[4, 8, 12, 16, 20, 24, 28, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384, 416, 448, 480, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1280, 1536, 1792, 2048, 2304, 2560, 2816, 3072, 3328, 3584, 3840, 4096, 4608, 5120, 5632, 6144, 6656, 7168, 7680, 8192], piecewise_cuda_graph_compiler='eager', torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, triton_attention_split_tile_size=None, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, enable_weights_cpu_backup=False, enable_draft_weights_cpu_backup=False, allow_auto_truncate=False, enable_custom_logit_processor=False, flashinfer_mla_disable_ragged=False, disable_shared_experts_fusion=False, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, keep_mm_feature_on_device=False, enable_return_hidden_states=False, enable_return_routed_experts=False, scheduler_recv_interval=1, numa_node=None, enable_deterministic_inference=False, rl_on_policy_target=None, enable_attn_tp_input_scattered=False, enable_nsa_prefill_context_parallel=False, nsa_prefill_cp_mode='round-robin-split', enable_fused_qk_norm_rope=False, enable_precise_embedding_interpolation=False, enable_dynamic_batch_tokenizer=False, dynamic_batch_tokenizer_batch_size=32, dynamic_batch_tokenizer_batch_timeout=0.002, debug_tensor_dump_output_folder=None, debug_tensor_dump_layers=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_transfer_backend='mooncake', disaggregation_bootstrap_port=8998, disaggregation_decode_tp=None, disaggregation_decode_dp=None, disaggregation_prefill_pp=1, disaggregation_ib_device=None, disaggregation_decode_enable_offload_kvcache=False, num_reserved_decode_tokens=512, disaggregation_decode_polling_interval=1, encoder_only=False, language_only=False, encoder_transfer_backend='zmq_to_scheduler', encoder_urls=[], custom_weight_loader=[], weight_loader_disable_mmap=False, remote_instance_weight_loader_seed_instance_ip=None, remote_instance_weight_loader_seed_instance_service_port=None, remote_instance_weight_loader_send_weights_group_ports=None, remote_instance_weight_loader_backend='nccl', remote_instance_weight_loader_start_seed_via_transfer_engine=False, enable_pdmux=False, pdmux_config_path=None, sm_group_num=8, mm_max_concurrent_calls=32, mm_per_request_timeout=10.0, enable_broadcast_mm_inputs_process=False, enable_prefix_mm_cache=False, mm_enable_dp_encoder=False, mm_process_config={}, limit_mm_data_per_request=None, enable_mm_global_cache=False, decrypted_config_file=None, decrypted_draft_config_file=None, forward_hooks=None)
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 50% Completed | 1/2 [00:01<00:01, 1.19s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:02<00:00, 1.28s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:02<00:00, 1.26s/it]
Capturing batches (bs=1 avail_mem=62.36 GB): 100%|██████████| 20/20 [00:01<00:00, 11.82it/s]
==== Original Output ====
First, I recognize that the user is asking for the sum of 1 and 3.
I will add the two numbers together to find the total.
Adding 1 and 3 gives me 4.
Therefore, the final answer is 4.
Sure! Let's solve the problem step by step.
**Question:** What is 1 + 3?
**Solution:**
1. **Identify the numbers to add:**
We have the numbers **1** and **3**.
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
I will add the two numbers together to find the total.
Adding 1 and 3 gives me 4.
Therefore, the final answer is 4.
Sure! Let's solve the problem step by step.
**Question:** What is 1 + 3?
**Solution:**
1. **Identify the numbers to add:**
We have the numbers **1** and **3**.
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
==== Reasoning ====
First, I recognize that the user is asking for the sum of 1 and 3.
I will add the two numbers together to find the total.
Adding 1 and 3 gives me 4.
Therefore, the final answer is 4.
I will add the two numbers together to find the total.
Adding 1 and 3 gives me 4.
Therefore, the final answer is 4.
==== Text ====
Sure! Let's solve the problem step by step.
**Question:** What is 1 + 3?
**Solution:**
1. **Identify the numbers to add:**
We have the numbers **1** and **3**.
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
**Question:** What is 1 + 3?
**Solution:**
1. **Identify the numbers to add:**
We have the numbers **1** and **3**.
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
[10]:
llm.shutdown()
Supporting New Reasoning Model Schemas#
For future reasoning models, you can implement the reasoning parser as a subclass of BaseReasoningFormatDetector in python/sglang/srt/reasoning_parser.py and specify the reasoning parser for new reasoning model schemas accordingly.