LMDeploy support chatting with PyTorch models with submodule lmdeploy.pytorch.chat
.
This submodule allow user to chat with language model through command line, and optionally accelerate model using backends like deepspeed.
Example 1: Chat with default setting
lmdeploy chat torch $PATH_TO_HF_MODEL
Example 2: Disable sampling and chat history
lmdeploy chat torch \
$PATH_TO_LLAMA_MODEL_IN_HF_FORMAT \
--temperature 0 --max-history 0
Example 3: Accelerate with deepspeed inference
lmdeploy chat torch \
$PATH_TO_LLAMA_MODEL_IN_HF_FORMAT \
--accel deepspeed
Note: to use deepspeed, you need to install deepspeed, and if hope to accelerate InternLM, you need a customized version https://github.com/wangruohui/DeepSpeed/tree/support_internlm_0.10.0
Example 4: Tensor parallel the model on 2 GPUs
deepspeed --module --num_gpus 2 lmdeploy.pytorch.chat \
$PATH_TO_LLAMA_MODEL_IN_HF_FORMAT \
--accel deepspeed \
This module also allow the following control commands to change generation behaviors during chat.
exit
: terminate and exit chatconfig set key=value
: change generation configkey
tovalue
, e.g. config temperature=0 disable sampling for following chatsclear
: clear chat history
graph LR;
subgraph model specific adapter
p((user_input))-->tokenize-->id((input_ids))-->decorate
tmpl_ids((template_ids))-->decorate;
end
subgraph generate
model[CausalLM_model.generate]-->gen_result(("gen_result"))
gen_result-->hid
gen_result-->attn((attention))
end
subgraph streamer
model-->s[streamer]--value-->decode_single--token-->output
end
subgraph session_manager
prepend_history-->fullid((complete_ids));
trim-->prepend_history
end
decorate-->prepend_history
hid((history_ids))-->trim;
attn-->trim;
fullid-->model
tokenizer((tokenizer))-->decode_single
tokenizer-->tokenize
p-->genconfig(GenConfig)-->model