Offline Inference
1. Usage
FastDeploy supports offline inference by loading models locally and processing user data. Usage examples:
Text Completion Interface (LLM.generate)
from fastdeploy import LLM, SamplingParams
prompts = [
"把李白的静夜思改写为现代诗",
"Write me a poem about large language model.",
]
# Sampling parameters
sampling_params = SamplingParams(top_p=0.95, max_tokens=6400)
# Load model
llm = LLM(model="ERNIE-4.5-0.3B", tensor_parallel_size=1, max_model_len=8192)
# Batch inference (internal request queuing and dynamic batching)
outputs = llm.generate(prompts, sampling_params)
# Output results
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
Chat Interface (LLM.chat)
from fastdeploy import LLM, SamplingParams
msg1=[
{"role": "system", "content": "I'm a helpful AI assistant."},
{"role": "user", "content": "把李白的静夜思改写为现代诗"},
]
msg2 = [
{"role": "system", "content": "I'm a helpful AI assistant."},
{"role": "user", "content": "Write me a poem about large language model."},
]
messages = [msg1, msg2]
# Sampling parameters
sampling_params = SamplingParams(top_p=0.95, max_tokens=6400)
# Load model
llm = LLM(model="ERNIE-4.5-0.3B", tensor_parallel_size=1, max_model_len=8192)
# Batch inference (internal request queuing and dynamic batching)
outputs = llm.chat(messages, sampling_params)
# Output results
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
Documentation for SamplingParams
, LLM.generate
, LLM.chat
, and output structure RequestOutput
is provided below.
Note: For X1 model output
# Output results
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
reasoning_text = output.outputs.resoning_content
2. API Documentation
2.1 fastdeploy.LLM
For LLM
configuration, refer to Parameter Documentation.
Configuration Notes: 1.
port
andmetrics_port
is only used for online inference. 2. After startup, the service logs KV Cache block count (e.g.total_block_num:640
). Multiply this by block_size (default 64) to get total cacheable tokens. 3. Calculatemax_num_seqs
based on cacheable tokens. Example: avg input=800 tokens, output=500 tokens, blocks=640 →kv_cache_ratio = 800/(800+500)=0.6
,max_seq_len = 640*64/(800+500)=31
.
2.2 fastdeploy.LLM.generate
- prompts(str,list[str],list[int]): Input prompts (batch supported), accepts decoded token ids
- sampling_params: See 2.4 for parameter details
- use_tqdm: Enable progress visualization
2.3 fastdeploy.LLM.chat
- messages(list[dict],list[list[dict]]): Input messages (batch supported)
- sampling_params: See 2.4 for parameter details
- use_tqdm: Enable progress visualization
- chat_template_kwargs(dict): Extra template parameters (currently supports enable_thinking(bool))
2.4 fastdeploy.SamplingParams
- presence_penalty(float): Penalizes repeated topics (positive values reduce repetition)
- frequency_penalty(float): Strict penalty for repeated tokens
- repetition_penalty(float): Direct penalty for repeated tokens (>1 penalizes, <1 encourages)
- temperature(float): Controls randomness (higher = more random)
- top_p(float): Probability threshold for token selection
- max_tokens(int): Maximum generated tokens (input + output)
- min_tokens(int): Minimum forced generation length
2.5 fastdeploy.engine.request.RequestOutput
- request_id(str): Request identifier
- prompt(str): Input content
- prompt_token_ids(list[int]): Tokenized input
- outputs(fastdeploy.engine.request.CompletionOutput): Results
- finished(bool): Completion status
- metrics(fastdeploy.engine.request.RequestMetrics): Performance metrics
- num_cached_tokens(int): Cached token count (only valid when enable_prefix_caching``` is enabled)
- error_code(int): Error code
- error_msg(str): Error message
2.6 fastdeploy.engine.request.CompletionOutput
- index(int): Batch index
- send_idx(int): Request token index
- token_ids(list[int]): Output tokens
- text(str): Decoded text
- reasoning_content(str): (X1 model only) Chain-of-thought output
2.7 fastdeploy.engine.request.RequestMetrics
- arrival_time(float): Request receipt time
- inference_start_time(float): Inference start time
- first_token_time(float): First token latency
- time_in_queue(float): Queuing time
- model_forward_time(float): Forward pass duration
- model_execute_time(float): Total execution time (including preprocessing)