mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 18:34:36 +00:00
6e7cca4047
* Implement customizable RoPE The original RoPE has pre-defined parameters theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2] Our customizable RoPE, ggml_rope_custom_inplace, uses theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2] with the default matches the original scale = 1.0 base = 10000 The new command line arguments --rope-freq-base --rope-freq-scale set the two new RoPE parameter. Recent researches show changing these two parameters extends the context limit with minimal loss. 1. Extending Context to 8K kaiokendev https://kaiokendev.github.io/til#extending-context-to-8k 2. Extending Context Window of Large Language Models via Positional Interpolation Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian https://arxiv.org/abs/2306.15595 3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation. https://www.reddit.com/user/bloc97 https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ For the bold, try adding the following command line parameters to your favorite model: -c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5 * ggml-metal: fix custom rope * common: fix argument names in help * llama: increase MEM_REQ_EVAL for MODEL_3B It avoids crashing for quantized weights on CPU. Better ways to calculate the required buffer size would be better. * llama: make MEM_REQ_EVAL depend on n_ctx * server: use proper Content-Type in curl examples Without the header Content-Type: application/json, curl will POST with Content-Type: application/x-www-form-urlencoded Though our simple server doesn't care, the httplib.h used has a limit with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 With Content-Type: application/json, we can send large json data. * style : minor fixes, mostly indentations * ggml : fix asserts --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
80 lines
1.9 KiB
Bash
80 lines
1.9 KiB
Bash
#!/bin/bash
|
|
|
|
API_URL="${API_URL:-http://127.0.0.1:8080}"
|
|
|
|
CHAT=(
|
|
"Hello, Assistant."
|
|
"Hello. How may I help you today?"
|
|
"Please tell me the largest city in Europe."
|
|
"Sure. The largest city in Europe is Moscow, the capital of Russia."
|
|
)
|
|
|
|
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
|
|
|
|
trim() {
|
|
shopt -s extglob
|
|
set -- "${1##+([[:space:]])}"
|
|
printf "%s" "${1%%+([[:space:]])}"
|
|
}
|
|
|
|
trim_trailing() {
|
|
shopt -s extglob
|
|
printf "%s" "${1%%+([[:space:]])}"
|
|
}
|
|
|
|
format_prompt() {
|
|
echo -n "${INSTRUCTION}"
|
|
printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1"
|
|
}
|
|
|
|
tokenize() {
|
|
curl \
|
|
--silent \
|
|
--request POST \
|
|
--url "${API_URL}/tokenize" \
|
|
--header "Content-Type: application/json" \
|
|
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
|
|
| jq '.tokens[]'
|
|
}
|
|
|
|
N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l)
|
|
|
|
chat_completion() {
|
|
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
|
|
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
|
|
prompt: .,
|
|
temperature: 0.2,
|
|
top_k: 40,
|
|
top_p: 0.9,
|
|
n_keep: $n_keep,
|
|
n_predict: 256,
|
|
stop: ["\n### Human:"],
|
|
stream: true
|
|
}')"
|
|
|
|
ANSWER=''
|
|
|
|
while IFS= read -r LINE; do
|
|
if [[ $LINE = data:* ]]; then
|
|
CONTENT="$(echo "${LINE:5}" | jq -r '.content')"
|
|
printf "%s" "${CONTENT}"
|
|
ANSWER+="${CONTENT}"
|
|
fi
|
|
done < <(curl \
|
|
--silent \
|
|
--no-buffer \
|
|
--request POST \
|
|
--url "${API_URL}/completion" \
|
|
--header "Content-Type: application/json" \
|
|
--data-raw "${DATA}")
|
|
|
|
printf "\n"
|
|
|
|
CHAT+=("$1" "$(trim "$ANSWER")")
|
|
}
|
|
|
|
while true; do
|
|
read -r -e -p "> " QUESTION
|
|
chat_completion "${QUESTION}"
|
|
done
|