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}")
[2026-01-24 15:01:21] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-01-24 15:01:21] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-01-24 15:01:21] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-01-24 15:01:27] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-01-24 15:01:27] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-01-24 15:01:27] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-01-24 15:01:29] INFO server_args.py:1771: Attention backend not specified. Use fa3 backend by default.
[2026-01-24 15:01:29] INFO server_args.py:2679: Set soft_watchdog_timeout since in CI
<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.
[2026-01-24 15:01:37] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-01-24 15:01:37] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-01-24 15:01:37] INFO utils.py:164: NumExpr defaulting to 16 threads.
[2026-01-24 15:01:38] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-01-24 15:01:38] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-01-24 15:01:38] INFO utils.py:164: NumExpr defaulting to 16 threads.
<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
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[2026-01-24 15:01:44] 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, 5.88s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:10<00:00, 5.15s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:10<00:00, 5.26s/it]
Capturing batches (bs=1 avail_mem=19.22 GB): 100%|██████████| 3/3 [00:00<00:00, 4.72it/s]
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 ====
First, I recognize that the problem is asking for the sum of the numbers 1 and 3.
Next, I add the two numbers together: 1 plus 3 equals 4.
Therefore, the final answer is 4.
Next, I add the two 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. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final Answer:**
\[
\boxed{4}
\]
We are asked to find the sum of 1 and 3.
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
**Final 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 of adding 1 and 3, I first identify the two numbers involved.
Next, I perform the addition operation by combining these numbers.
Finally, I calculate the sum to find the result.
Next, I perform the addition operation by combining these numbers.
Finally, I calculate the sum to find the result.
==== Text ====
Sure! Let's solve the problem step by step.
**Question:** What is \(1 + 3\)?
**Solution:**
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Perform the addition:**
\[
1 + 3 = 4
\]
**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 recognize that the problem is asking for the sum of 1 and 3.
Next, I add the two numbers together: 1 plus 3 equals 4.
Therefore, the final answer is 4.
Next, I add the two 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 \(3\).
\[
1 + 3 = 4
\]
Therefore, the final answer is \(\boxed{4}\).
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 ====
First, I recognize that the user is asking for the sum of 1 and 3.
I'll start by identifying the two numbers involved in the addition: 1 and 3.
Next, I'll perform the addition by combining these two numbers together.
Finally, I'll calculate the total to determine the result.
Sure! Let's solve the addition problem step by step.
**Question:** What is \(1 + 3\)?
**Solution:**
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Final Answer:**
\[
\boxed{4}
\]
I'll start by identifying the two numbers involved in the addition: 1 and 3.
Next, I'll perform the addition by combining these two numbers together.
Finally, I'll calculate the total to determine the result.
Sure! Let's solve the addition problem step by step.
**Question:** What is \(1 + 3\)?
**Solution:**
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Final Answer:**
\[
\boxed{4}
\]
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 I need to calculate the sum of 1 and 3.
Adding these two numbers together gives me 4.
Therefore, the result of 1 plus 3 is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
Therefore, the result is \(\boxed{4}\).
Adding these two numbers together gives me 4.
Therefore, the result of 1 plus 3 is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
Therefore, the result is \(\boxed{4}\).
==== Reasoning ====
First, I recognize that I need to calculate the sum of 1 and 3.
Adding these two numbers together gives me 4.
Therefore, the result of 1 plus 3 is 4.
Adding these two numbers together gives me 4.
Therefore, the result of 1 plus 3 is 4.
==== Text ====
**Solution:**
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
Therefore, the result is \(\boxed{4}\).
We are asked to find the sum of 1 and 3.
\[
1 + 3 = 4
\]
Therefore, the result is \(\boxed{4}\).
[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.
[2026-01-24 15:02:12] INFO server_args.py:1771: Attention backend not specified. Use fa3 backend by default.
[2026-01-24 15:02:12] INFO server_args.py:2679: Set soft_watchdog_timeout since in CI
[2026-01-24 15:02:12] INFO engine.py:154: 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=479190504, 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, 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', 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='auto', nsa_prefill_backend=None, nsa_decode_backend=None, disable_flashinfer_autotune=False, 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, 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, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype='float32', mamba_full_memory_ratio=0.9, mamba_scheduler_strategy='no_buffer', mamba_track_interval=256, 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, 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='in-seq-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, disaggregation_decode_enable_fake_auto=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, 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
[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, 1.27s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:02<00:00, 1.38s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:02<00:00, 1.36s/it]
Capturing batches (bs=1 avail_mem=43.10 GB): 100%|██████████| 20/20 [00:01<00:00, 17.16it/s]
==== Original Output ====
I recognize that the problem is asking for the sum of 1 and 3.
I start by identifying the two numbers involved, which are 1 and 3.
Next, I add these two numbers together to find the total.
Finally, I conclude that the sum of 1 and 3 is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Present the final answer:**
\[
\boxed{4}
\]
I start by identifying the two numbers involved, which are 1 and 3.
Next, I add these two numbers together to find the total.
Finally, I conclude that the sum of 1 and 3 is 4.
**Solution:**
We are asked to find the sum of 1 and 3.
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Present the final answer:**
\[
\boxed{4}
\]
==== Reasoning ====
I recognize that the problem is asking for the sum of 1 and 3.
I start by identifying the two numbers involved, which are 1 and 3.
Next, I add these two numbers together to find the total.
Finally, I conclude that the sum of 1 and 3 is 4.
I start by identifying the two numbers involved, which are 1 and 3.
Next, I add these two numbers together to find the total.
Finally, I conclude that the sum of 1 and 3 is 4.
==== Text ====
**Solution:**
We are asked to find the sum of 1 and 3.
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Present the final answer:**
\[
\boxed{4}
\]
We are asked to find the sum of 1 and 3.
1. **Identify the numbers to add:**
\[
1 \quad \text{and} \quad 3
\]
2. **Add the numbers together:**
\[
1 + 3 = 4
\]
3. **Present the 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.