vbatts/llama.cpp
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Merge branch 'master' into mpi

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This commit is contained in:
Evan Miller 2023-07-06 19:04:50 -04:00
commit 32deabfdc8
33 changed files with 5794 additions and 2048 deletions
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2
examples/alpaca.sh
View file

@ -7,7 +7,7 @@
cd `dirname $0`
cd ..
./main -m ./models/ggml-alpaca-7b-q4.bin \
./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
--color \
-f ./prompts/alpaca.txt \
--ctx_size 2048 \

2
examples/embd-input/embd-input-lib.cpp
View file

@ -29,7 +29,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

2
examples/embedding/embedding.cpp
View file

@ -18,7 +18,7 @@ int main(int argc, char ** argv) {
params.embedding = true;
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}

2
examples/main/main.cpp
View file

@ -85,7 +85,7 @@ int main(int argc, char ** argv) {
}
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);

2
examples/perplexity/perplexity.cpp
View file

@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
params.n_batch = std::min(params.n_batch, params.n_ctx);
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}

14
examples/quantize-stats/quantize-stats.cpp
View file

@ -147,7 +147,7 @@ void test_roundtrip_on_chunk(
const ggml_tensor * layer,
int64_t offset,
int64_t chunk_size,
const quantize_fns_t & qfns,
const ggml_type_traits_t & qfns,
bool use_reference,
float * input_scratch,
char * quantized_scratch,
@ -163,11 +163,11 @@ void test_roundtrip_on_chunk(
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
qfns.from_float(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
qfns.to_float(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
@ -177,7 +177,7 @@ void test_roundtrip_on_chunk(
void test_roundtrip_on_layer(
std::string & name,
bool print_layer_stats,
const quantize_fns_t & qfns,
const ggml_type_traits_t & qfns,
bool use_reference,
const ggml_tensor * layer,
std::vector<float> & input_scratch,
@ -388,8 +388,8 @@ int main(int argc, char ** argv) {
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (qfns.from_float && qfns.to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
}

60
examples/server/README.md
View file

@ -1,13 +1,13 @@
# llama.cpp/example/server
This example demonstrates a simple HTTP API server to interact with llama.cpp.
This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
Command line options:
- `--threads N`, `-t N`: Set the number of threads to use during computation.
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
@ -21,24 +21,22 @@ Command line options:
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default examples/server/public)
- `--embedding`: Enable embedding extraction, Default: disabled.
## Build
Build llama.cpp with server from repository root with either make or CMake.
server is build alongside everything else from the root of the project
- Using `make`:
```bash
LLAMA_BUILD_SERVER=1 make
make
```
- Using `CMake`:
```bash
mkdir build-server
cd build-server
cmake -DLLAMA_BUILD_SERVER=ON ..
cmake --build . --config Release
```
@ -59,7 +57,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048
```
The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
## Testing with CURL
@ -190,3 +188,49 @@ Run with bash:
```sh
bash chat.sh
```
### API like OAI
API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
This example must be used with server.cpp
```sh
python api_like_OAI.py
```
After running the API server, you can use it in Python by setting the API base URL.
```python
openai.api_base = "http://<Your api-server IP>:port"
```
Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
### Extending or building alternative Web Front End
The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
Read the documentation in `/completion.js` to see convenient ways to access llama.
A simple example is below:
```html
<html>
<body>
<pre>
<script type="module">
import { llama } from '/completion.js'
const prompt = `### Instruction:
Write dad jokes, each one paragraph.
You can use html formatting if needed.
### Response:`
for await (const chunk of llama(prompt)) {
document.write(chunk.data.content)
}
</script>
</pre>
</body>
</html>
```

219
examples/server/api_like_OAI.py Executable file
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@ -0,0 +1,219 @@
import argparse
from flask import Flask, jsonify, request, Response
import urllib.parse
import requests
import time
import json
app = Flask(__name__)
parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
args = parser.parse_args()
def is_present(json, key):
try:
buf = json[key]
except KeyError:
return False
return True
#convert chat to prompt
def convert_chat(messages):
prompt = "" + args.chat_prompt.replace("\\n", "\n")
system_n = args.system_name.replace("\\n", "\n")
user_n = args.user_name.replace("\\n", "\n")
ai_n = args.ai_name.replace("\\n", "\n")
stop = args.stop.replace("\\n", "\n")
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
return prompt
def make_postData(body, chat=False, stream=False):
postData = {}
if (chat):
postData["prompt"] = convert_chat(body["messages"])
else:
postData["prompt"] = body["prompt"]
if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
else:
postData["stop"] = []
if(is_present(body, "stop")): postData["stop"] += body["stop"]
postData["n_keep"] = -1
postData["stream"] = stream
return postData
def make_resData(data, chat=False, promptToken=[]):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion" if (chat) else "text_completion",
"created": int(time.time()),
"truncated": data["truncated"],
"model": "LLaMA_CPP",
"usage": {
"prompt_tokens": data["tokens_evaluated"],
"completion_tokens": data["tokens_predicted"],
"total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
}
}
if (len(promptToken) != 0):
resData["promptToken"] = promptToken
if (chat):
#only one choice is supported
resData["choices"] = [{
"index": 0,
"message": {
"role": "assistant",
"content": data["content"],
},
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
else:
#only one choice is supported
resData["choices"] = [{
"text": data["content"],
"index": 0,
"logprobs": None,
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
return resData
def make_resData_stream(data, chat=False, time_now = 0, start=False):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
"created": time_now,
"model": "LLaMA_CPP",
"choices": [
{
"finish_reason": None,
"index": 0
}
]
}
if (chat):
if (start):
resData["choices"][0]["delta"] = {
"role": "assistant"
}
else:
resData["choices"][0]["delta"] = {
"content": data["content"]
}
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
else:
resData["choices"][0]["text"] = data["content"]
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
return resData
@app.route('/chat/completions', methods=['POST'])
@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=True, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=True, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n'.format(json.dumps(resData))
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
@app.route('/completions', methods=['POST'])
@app.route('/v1/completions', methods=['POST'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=False, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=False, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
if __name__ == '__main__':
app.run(args.host, port=args.port)

375
examples/server/completion.js.hpp Normal file
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@ -0,0 +1,375 @@
unsigned char completion_js[] = {
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unsigned int completion_js_len = 4462;

18
examples/server/deps.sh Executable file
View file

@ -0,0 +1,18 @@
#!/bin/bash
# Download and update deps for binary
# get the directory of this script file
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
PUBLIC=$DIR/public
echo "download js bundle files"
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
echo >> $PUBLIC/index.js # add newline
FILES=$(ls $PUBLIC)
for FILE in $FILES; do
func=$(echo $FILE | tr '.' '_')
echo "generate $FILE.hpp ($func)"
xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
done

899
examples/server/index.html.hpp Normal file
View file

@ -0,0 +1,899 @@
unsigned char index_html[] = {
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};
unsigned int index_html_len = 10752;

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const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
let generation_settings = null;
// Completes the prompt as a generator. Recommended for most use cases.
//
// Example:
//
// import { llama } from '/completion.js'
//
// const request = llama("Tell me a joke", {n_predict: 800})
// for await (const chunk of request) {
// document.write(chunk.data.content)
// }
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
if (!controller) {
controller = new AbortController();
}
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch("/completion", {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
},
signal: controller.signal,
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let content = "";
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
// mainly care about the data: key here, which we expect as json
const text = decoder.decode(result.value);
// parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const match of text.matchAll(regex)) {
result[match[1]] = match[2]
}
// since we know this is llama.cpp, let's just decode the json in data
result.data = JSON.parse(result.data);
content += result.data.content;
// yield
yield result;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
}
break;
}
}
} catch (e) {
if (e.name !== 'AbortError') {
console.error("llama error: ", e);
}
throw e;
}
finally {
controller.abort();
}
return content;
}
// Call llama, return an event target that you can subcribe to
//
// Example:
//
// import { llamaEventTarget } from '/completion.js'
//
// const conn = llamaEventTarget(prompt)
// conn.addEventListener("message", (chunk) => {
// document.write(chunk.detail.content)
// })
//
export const llamaEventTarget = (prompt, params = {}, config = {}) => {
const eventTarget = new EventTarget();
(async () => {
let content = "";
for await (const chunk of llama(prompt, params, config)) {
if (chunk.data) {
content += chunk.data.content;
eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
}
if (chunk.data.generation_settings) {
eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
}
if (chunk.data.timings) {
eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
}
}
eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
})();
return eventTarget;
}
// Call llama, return a promise that resolves to the completed text. This does not support streaming
//
// Example:
//
// llamaPromise(prompt).then((content) => {
// document.write(content)
// })
//
// or
//
// const content = await llamaPromise(prompt)
// document.write(content)
//
export const llamaPromise = (prompt, params = {}, config = {}) => {
return new Promise(async (resolve, reject) => {
let content = "";
try {
for await (const chunk of llama(prompt, params, config)) {
content += chunk.data.content;
}
resolve(content);
} catch (error) {
reject(error);
}
});
};
/**
* (deprecated)
*/
export const llamaComplete = async (params, controller, callback) => {
for await (const chunk of llama(params.prompt, params, { controller })) {
callback(chunk);
}
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
if (!generation_settings) {
generation_settings = await fetch("/model.json").then(r => r.json());
}
return generation_settings;
}

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<html>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<title>llama.cpp - chat</title>
<style>
body {
background-color: #fff;
color: #000;
font-family: system-ui;
font-size: 90%;
}
#container {
margin: 0em auto;
display: flex;
flex-direction: column;
justify-content: space-between;
height: 100%;
}
main {
margin: 3px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 1em;
flex-grow: 1;
overflow-y: auto;
border: 1px solid #ccc;
border-radius: 5px;
padding: 0.5em;
}
body {
max-width: 600px;
min-width: 300px;
line-height: 1.2;
margin: 0 auto;
padding: 0 0.5em;
}
p {
overflow-wrap: break-word;
word-wrap: break-word;
hyphens: auto;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
#write form {
margin: 1em 0 0 0;
display: flex;
flex-direction: column;
gap: 0.5em;
align-items: stretch;
}
.right {
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
}
fieldset {
border: none;
padding: 0;
margin: 0;
}
textarea {
padding: 5px;
flex-grow: 1;
width: 100%;
}
pre code {
display: block;
background-color: #222;
color: #ddd;
}
code {
font-family: monospace;
padding: 0.1em 0.3em;
border-radius: 3px;
}
fieldset label {
margin: 0.5em 0;
display: block;
}
header, footer {
text-align: center;
}
footer {
font-size: 80%;
color: #888;
}
</style>
<script type="module">
import {
html, h, signal, effect, computed, render, useSignal, useEffect, useRef
} from '/index.js';
import { llama } from '/completion.js';
const session = signal({
prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
template: "{{prompt}}\n\n{{history}}\n{{char}}:",
historyTemplate: "{{name}}: {{message}}",
transcript: [],
type: "chat",
char: "llama",
user: "User",
})
const params = signal({
n_predict: 400,
temperature: 0.7,
repeat_last_n: 256,
repeat_penalty: 1.18,
top_k: 40,
top_p: 0.5,
})
const llamaStats = signal(null)
const controller = signal(null)
const generating = computed(() => controller.value == null )
const chatStarted = computed(() => session.value.transcript.length > 0)
const transcriptUpdate = (transcript) => {
session.value = {
...session.value,
transcript
}
}
// simple template replace
const template = (str, extraSettings) => {
let settings = session.value;
if (extraSettings) {
settings = { ...settings, ...extraSettings };
}
return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
}
// send message to server
const chat = async (msg) => {
if (controller.value) {
console.log('already running...');
return;
}
controller.value = new AbortController();
transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
const prompt = template(session.value.template, {
message: msg,
history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
});
let currentMessage = '';
const history = session.value.transcript
const llamaParams = {
...params.value,
stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
}
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
const data = chunk.data;
currentMessage += data.content;
// remove leading whitespace
currentMessage = currentMessage.replace(/^\s+/, "")
transcriptUpdate([...history, ["{{char}}", currentMessage]])
if (data.stop) {
console.log("Completion finished: '", currentMessage, "', summary: ", data);
}
if (data.timings) {
llamaStats.value = data.timings;
}
}
controller.value = null;
}
function MessageInput() {
const message = useSignal("")
const stop = (e) => {
e.preventDefault();
if (controller.value) {
controller.value.abort();
controller.value = null;
}
}
const reset = (e) => {
stop(e);
transcriptUpdate([]);
}
const submit = (e) => {
stop(e);
chat(message.value);
message.value = "";
}
const enterSubmits = (event) => {
if (event.which === 13 && !event.shiftKey) {
submit(event);
}
}
return html`
<form onsubmit=${submit}>
<div>
<textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
</div>
<div class="right">
<button type="submit" disabled=${!generating.value} >Send</button>
<button onclick=${stop} disabled=${generating}>Stop</button>
<button onclick=${reset}>Reset</button>
</div>
</form>
`
}
const ChatLog = (props) => {
const messages = session.value.transcript;
const container = useRef(null)
useEffect(() => {
// scroll to bottom (if needed)
if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
container.current.scrollTo(0, container.current.scrollHeight)
}
}, [messages])
const chatLine = ([user, msg]) => {
return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
};
return html`
<section id="chat" ref=${container}>
${messages.flatMap(chatLine)}
</section>`;
};
const ConfigForm = (props) => {
const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
return html`
<form>
<fieldset>
<div>
<label for="prompt">Prompt</label>
<textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="user">User name</label>
<input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
</div>
<div>
<label for="bot">Bot name</label>
<input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
</div>
<div>
<label for="template">Prompt template</label>
<textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="template">Chat history template</label>
<textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
</div>
<div>
<label for="temperature">Temperature</label>
<input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
<span>${params.value.temperature}</span>
</div>
<div>
<label for="nPredict">Predictions</label>
<input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
<span>${params.value.n_predict}</span>
</div>
<div>
<label for="repeat_penalty">Penalize repeat sequence</label>
<input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_penalty}</span>
</div>
<div>
<label for="repeat_last_n">Consider N tokens for penalize</label>
<input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_last_n}</span>
</div>
</fieldset>
</form>
`
}
// poor mans markdown replacement
const Markdownish = (params) => {
const md = params.text
.replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
.replace(/__(.*?)__/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*/g, '<em>$1</em>')
.replace(/_(.*?)_/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
};
const ModelGenerationInfo = (params) => {
if (!llamaStats.value) {
return html`<span/>`
}
return html`
<span>
${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
</span>
`
}
function App(props) {
return html`
<div id="container">
<header>
<h1>llama.cpp</h1>
</header>
<main id="content">
<${chatStarted.value ? ChatLog : ConfigForm} />
</main>
<section id="write">
<${MessageInput} />
</section>
<footer>
<p><${ModelGenerationInfo} /></p>
<p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
</footer>
</div>
`;
}
render(h(App), document.body);
</script>
</head>
<body>
</body>
</html>

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