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ParisNeo
parent
325cd5b627
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59f9413253
16
app.py
16
app.py
@ -230,27 +230,33 @@ class Gpt4AllWebUI(GPT4AllAPI):
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response = requests.get(model_path, stream=True)
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file_size = int(response.headers.get('Content-Length'))
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downloaded_size = 0
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CHUNK_SIZE = 4096
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CHUNK_SIZE = 8192
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def download_chunk(url, start_byte, end_byte, fileobj):
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headers = {'Range': f'bytes={start_byte}-{end_byte}'}
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response = requests.get(url, headers=headers, stream=True)
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downloaded_bytes = 0
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for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
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if chunk:
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fileobj.seek(start_byte)
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fileobj.write(chunk)
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downloaded_bytes += len(chunk)
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start_byte += len(chunk)
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return downloaded_bytes
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def download_file(url, file_path, num_threads=4):
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response = requests.head(url)
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file_size = int(response.headers.get('Content-Length'))
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chunk_size = file_size // num_threads
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progress = 0
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with open(file_path, 'wb') as fileobj:
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with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024) as pbar:
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with ThreadPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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chunk_size = file_size // num_threads
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for i in range(num_threads):
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start_byte = i * chunk_size
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@ -258,8 +264,10 @@ class Gpt4AllWebUI(GPT4AllAPI):
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futures.append(executor.submit(download_chunk, url, start_byte, end_byte, fileobj))
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for future in tqdm(as_completed(futures), total=num_threads):
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future.result()
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pbar.update(chunk_size)
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downloaded_bytes = future.result()
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progress += downloaded_bytes
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pbar.update(downloaded_bytes)
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socketio.emit('install_progress', {'status': 'progress', 'progress': progress})
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# Usage example
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download_file(model_path, installation_path, num_threads=4)
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@ -53,6 +53,7 @@ export default {
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isInstalled: Boolean,
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onInstall: Function,
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onUninstall: Function,
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onSelected: Function,
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selected: Boolean
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},
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data() {
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@ -75,7 +76,8 @@ export default {
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},
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handleSelection() {
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if (this.isInstalled && !this.selected) {
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this.$emit('update:selected', true);
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this.selected=true;
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onSelected(this);
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}
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}
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}
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@ -52,17 +52,21 @@
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</select>
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</div>
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<div class="m-2">
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<label for="model" class="block mb-2 text-sm font-medium text-gray-900 dark:text-white">
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Model:
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</label>
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<select id="model"
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class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500">
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</div>
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</div>
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<div
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class="flex flex-col mb-2 p-3 rounded-lg bg-bg-light-tone dark:bg-bg-dark-tone hover:bg-bg-light-tone-panel hover:dark:bg-bg-dark-tone-panel duration-150 shadow-lg">
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<div class="flex flex-row ">
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<button @click.stop="bec_collapsed = !bec_collapsed"
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class="text-2xl hover:text-primary duration-75 p-2 -m-2 w-full text-left active:translate-y-1">
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<!-- <i data-feather="chevron-right"></i> -->
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<option v-for="item in modelsArr" :selected="item === configFile.model">{{ item }}</option>
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</select>
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</div>
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<h3 class="text-lg font-semibold cursor-pointer select-none "
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@click.stop="bec_collapsed = !bec_collapsed">
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Models zoo</h3>
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</button>
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</div>
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<div :class="{ 'hidden': bec_collapsed }" class="flex flex-col mb-2 p-2">
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<div v-if="models.length > 0" class="my-2">
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<label for="model" class="block ml-2 mb-2 text-sm font-medium text-gray-900 dark:text-white">
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Install more models:
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@ -71,12 +75,12 @@
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<model-entry v-for="(model, index) in models" :key="index" :title="model.title" :icon="model.icon"
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:path="model.path" :description="model.description" :is-installed="model.isInstalled"
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:on-install="onInstall"
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:on-uninstall="onUninstall" />
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:on-uninstall="onUninstall"
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:on-selected="onSelected" />
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</div>
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</div>
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</div>
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</div>
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<!-- PERSONALITY -->
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<div
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class="flex flex-col mb-2 p-3 rounded-lg bg-bg-light-tone dark:bg-bg-dark-tone hover:bg-bg-light-tone-panel hover:dark:bg-bg-dark-tone-panel duration-150 shadow-lg">
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@ -321,7 +325,6 @@ export default {
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mc_collapsed: false,
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// Settings stuff
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backendsArr: [],
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modelsArr: [],
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persLangArr: [],
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persCatgArr: [],
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persArr: [],
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@ -343,6 +346,9 @@ export default {
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console.log(error);
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});
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},
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onSelected(model_object){
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update_setting('model', model_object.title)
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},
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// Model installation
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onInstall(model_object) {
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let isInstalled = model_object.isInstalled
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@ -354,7 +360,7 @@ export default {
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const progressListener = (response) => {
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if (response.status === 'progress') {
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this.progress = message.progress;
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} else if (message.status === 'succeeded') {
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} else if (response.status === 'succeeded') {
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// Installation completed
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model_object.installing = false;
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this.showProgress = false;
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@ -363,7 +369,7 @@ export default {
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this.models[index].isInstalled = true;
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this.socket.off('install_progress', progressListener);
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} else if (message.status === 'failed') {
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} else if (response.status === 'failed') {
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// Installation failed or encountered an error
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model_object.installing = false;
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this.showProgress = false;
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@ -381,22 +387,21 @@ export default {
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const progressListener = (response) => {
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if (response.status === 'progress') {
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this.progress = message.progress;
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} else if (message.status === 'succeeded') {
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// Installation completed
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model_object.uninstalling = false;
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} else if (response.status === 'succeeded') {
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// Installation completed
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model_object.uninstalling = false;
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this.showProgress = false;
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// Update the isInstalled property of the corresponding model
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const index = this.models.findIndex((model) => model.path === path);
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this.models[index].isInstalled = false;
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this.showProgress = false;
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// Update the isInstalled property of the corresponding model
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model_object.isInstalled = false;
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this.socket.off('install_progress', progressListener);
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} else if (message.status === 'failed') {
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// Installation failed or encountered an error
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model_object.uninstalling = false;
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this.showProgress = false;
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this.socket.off('install_progress', progressListener);
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console.error('Installation failed:', message.error);
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this.socket.off('install_progress', progressListener);
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} else if (response.status === 'failed') {
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// Installation failed or encountered an error
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model_object.uninstalling = false;
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this.showProgress = false;
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this.socket.off('install_progress', progressListener);
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console.error('Installation failed:', message.error);
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}
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};
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this.socket.on('install_progress', progressListener);
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@ -417,7 +422,19 @@ export default {
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//this.api_get_req("list_personalities_categories").then(response => { this.persCatgArr = response })
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//this.api_get_req("list_personalities").then(response => { this.persArr = response })
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//this.api_get_req("list_languages").then(response => { this.langArr = response })
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this.api_get_req("get_config").then(response => { this.configFile = response })
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this.api_get_req("get_config").then(response => {
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this.configFile = response
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console.log("selecting model")
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self.models.forEach(model => {
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console.log(`${model} -> ${response["model"]}`)
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if(model.title==response["model"]){
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model.selected=true;
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}
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else{
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model.selected=false;
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}
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});
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})
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},
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// Accordeon stuff
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toggleAccordion() {
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@ -516,7 +533,6 @@ export default {
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this.configFile = await this.api_get_req("get_config")
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this.backendsArr = await this.api_get_req("list_backends")
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this.modelsArr = await this.api_get_req("list_models")
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this.persLangArr = await this.api_get_req("list_personalities_languages")
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this.persCatgArr = await this.api_get_req("list_personalities_categories")
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this.persArr = await this.api_get_req("list_personalities")
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88
zoos/gpt4all_models_zoo.json
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88
zoos/gpt4all_models_zoo.json
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@ -0,0 +1,88 @@
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[
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{
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"md5sum": "81a09a0ddf89690372fc296ff7f625af",
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"filename": "ggml-gpt4all-j-v1.3-groovy.bin",
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"filesize": "3785248281",
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"isDefault": "true",
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"bestGPTJ": "true",
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"description": "Current best commercially licensable model based on GPT-J and trained by Nomic AI on the latest curated GPT4All dataset."
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},
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{
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"md5sum": "91f886b68fbce697e9a3cd501951e455",
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"filename": "ggml-gpt4all-l13b-snoozy.bin",
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"filesize": "8136770688",
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"bestLlama": "true",
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"description": "Current best non-commercially licensable model based on Llama 13b and trained by Nomic AI on the latest curated GPT4All dataset."
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},
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{
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"md5sum": "756249d3d6abe23bde3b1ae272628640",
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"filename": "ggml-mpt-7b-chat.bin",
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"filesize": "4854401050",
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"isDefault": "true",
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"bestMPT": "true",
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"requires": "2.4.1",
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"description": "Current best non-commercially licensable chat model based on MPT and trained by Mosaic ML."
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},
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{
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"md5sum": "879344aaa9d62fdccbda0be7a09e7976",
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"filename": "ggml-gpt4all-j-v1.2-jazzy.bin",
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"filesize": "3785248281",
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"description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v2 GPT4All dataset."
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},
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{
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"md5sum": "61d48a82cb188cceb14ebb8082bfec37",
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"filename": "ggml-gpt4all-j-v1.1-breezy.bin",
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"filesize": "3785248281",
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"description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v1 GPT4All dataset."
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},
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{
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"md5sum": "5b5a3f9b858d33b29b52b89692415595",
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"filename": "ggml-gpt4all-j.bin",
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"filesize": "3785248281",
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"description": "A commercially licensable model based on GPT-J and trained by Nomic AI on the v0 GPT4All dataset."
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},
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{
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"md5sum": "29119f8fa11712704c6b22ac5ab792ea",
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"filename": "ggml-vicuna-7b-1.1-q4_2.bin",
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"filesize": "4212859520",
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"description": "A non-commercially licensable model based on Llama 7b and trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego."
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},
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{
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"md5sum": "95999b7b0699e2070af63bf5d34101a8",
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"filename": "ggml-vicuna-13b-1.1-q4_2.bin",
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"filesize": "8136770688",
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"description": "A non-commercially licensable model based on Llama 13b and trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego."
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},
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{
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"md5sum": "99e6d129745a3f1fb1121abed747b05a",
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"filename": "ggml-wizardLM-7B.q4_2.bin",
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"filesize": "4212864640",
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"description": "A non-commercially licensable model based on Llama 7b and trained by Microsoft and Peking University."
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},
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{
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"md5sum": "6cb4ee297537c9133bddab9692879de0",
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"filename": "ggml-stable-vicuna-13B.q4_2.bin",
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"filesize": "8136777088",
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"description": "A non-commercially licensable model based on Llama 13b and RLHF trained by Stable AI."
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},
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{
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"md5sum": "120c32a51d020066288df045ef5d52b9",
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"filename": "ggml-mpt-7b-base.bin",
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"filesize": "4854401028",
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"requires": "2.4.1",
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"description": "A commercially licensable model base pre-trained by Mosaic ML."
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},
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{
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"md5sum": "d5eafd5b0bd0d615cfd5fd763f642dfe",
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"filename": "ggml-nous-gpt4-vicuna-13b.bin",
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"filesize": "8136777088",
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"description": "A non-commercially licensable model based on Vicuna 13b, fine-tuned on ~180,000 instructions, trained by Nous Research."
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},
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{
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"md5sum": "1cfa4958f489f0a0d1ffdf6b37322809",
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"filename": "ggml-mpt-7b-instruct.bin",
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"filesize": "4854401028",
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"requires": "2.4.1",
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"description": "A commericially licensable instruct model based on MPT and trained by Mosaic ML."
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}
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]
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