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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
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API_URL="${API_URL:-http://127.0.0.1:8080}"
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CHAT=(
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"Hello, Assistant."
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"Hello. How may I help you today?"
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"Please tell me the largest city in Europe."
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"Sure. The largest city in Europe is Moscow, the capital of Russia."
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)
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INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
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trim() {
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shopt -s extglob
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set -- "${1##+([[:space:]])}"
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printf "%s" "${1%%+([[:space:]])}"
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}
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trim_trailing() {
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shopt -s extglob
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printf "%s" "${1%%+([[:space:]])}"
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}
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format_prompt() {
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echo -n "${INSTRUCTION}"
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printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1"
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}
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tokenize() {
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curl \
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--silent \
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--request POST \
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--url "${API_URL}/tokenize" \
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--header "Content-Type: application/json" \
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--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
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| jq '.tokens[]'
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}
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N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l)
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chat_completion() {
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PROMPT="$(trim_trailing "$(format_prompt "$1")")"
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DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
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prompt: .,
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temperature: 0.2,
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top_k: 40,
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top_p: 0.9,
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n_keep: $n_keep,
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n_predict: 256,
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stop: ["\n### Human:"],
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stream: true
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}')"
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ANSWER=''
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while IFS= read -r LINE; do
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if [[ $LINE = data:* ]]; then
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CONTENT="$(echo "${LINE:5}" | jq -r '.content')"
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printf "%s" "${CONTENT}"
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ANSWER+="${CONTENT}"
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fi
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done < <(curl \
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--silent \
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--no-buffer \
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--request POST \
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--url "${API_URL}/completion" \
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--header "Content-Type: application/json" \
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--data-raw "${DATA}")
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printf "\n"
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CHAT+=("$1" "$(trim "$ANSWER")")
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}
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while true; do
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read -r -e -p "> " QUESTION
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chat_completion "${QUESTION}"
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done
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