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enhanced download of models, updated model list
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Michael Peter Christen committed
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commit
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1 file changed
+172
-51
+172
-51
@@ -97,23 +97,20 @@
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let persistedModelCapabilities = {};
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const RECOMMENDED_MODELS = [
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//["hf.co/tiiuae/Falcon-H1-0.5B-Instruct-GGUF:Q4_K_M", " 0.50","0.5GB", "english-only minimalistic model for small devices", "Technology Innovation Institute, Dubai", "falcon-llm-license"],
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//["llama3.2:1b-instruct-q4_K_M", " 0.10", "2GB", "A good 1B model", "Meta", "llama3.2"],
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["qwen2.5:1.5b-instruct-q4_K_M", " 0.88", "2GB", "Good small model for 1GB RAM", "Alibaba", "apache-2.0"],
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["llama3.2:3b-instruct-q4_K_M", " 0.66", "3GB", "A good 3B model", "Meta", "llama3.2"],
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//["qwen3-vl:2b-instruct-q4_K_M", " 0.73", "3GB", "A very small vision-model, can understand what is sees in images"],
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["qwen2.5:1.5b-instruct-q4_K_M", " 0.88", "1GB", "Good small model for 2GB RAM", "Alibaba", "apache-2.0"],
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["llama3.2:3b-instruct-q4_K_M", " 0.66", "2GB", "A good 3B model", "Meta", "llama3.2"],
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["qwen3:4b-instruct-2507-q4_K_M", " 7.70", "3GB", "a good 4B model", "Alibaba", "apache-2.0"],
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["hf.co/janhq/Jan-v3-4B-base-instruct-gguf:Q4_K_M", " 8.19", "3GB", "a brilliant 4B model, post-trained with large teacher from qwen3:4b", "jan.ai", "apache-2.0"],
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["hf.co/unsloth/medgemma-4b-it-GGUF:Q4_K_M", " 0.60", "4GB", "Medical Knowledge and Vision", "Google", "health-ai-developer-foundations"],
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["olmo-3:7b-instruct-q4_K_M", " 2.22", "4GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"],
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["qwen3:4b-instruct-2507-q4_K_M", " 7.70", "3GB", "a good 4B model", "Alibaba", "apache-2.0"],
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["hf.co/janhq/Jan-v3-4B-base-instruct-gguf:Q4_K_M", " 8.19", "6GB", "a brilliant 4B model, post-trained with large teacher from qwen3:4b", "jan.ai", "apache-2.0"],
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["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European flagship model, strong multilangual, vision, agentic", "mistral.ai", "apache-2.0"],
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["olmo-3.1:32b-instruct-q4_K_M", "10.25", "20GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"],
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//["phi4:14b-q4_K_M", " 5.24", "10GB", "Strong, made with synthetic data", "Microsoft", "mit"],
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//["gemma3:27b-it-q4_K_M", " 4.81", "20GB", "Strong content safety, multilingual support in over 140 languages", "google.com", "gemma"],
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//["qwen3-vl:30b-a3b-instruct-q4_K_M", "17.33", "22GB", "Very fast, exceptional good 30B model, ranking above GPT-4-turbo, GPT-4.1-nano, GPT-o1, GPT-4o-mini", "Alibaba", "apache-2.0"],
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["qwen3.5:9b-q4_K_M", "18.37", "14GB", "multimodal, outstanding for its size, long-context 256K Tokens, strong instruction following model", "Alibaba", "apache-2.0"],
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["hf.co/mradermacher/Ling-mini-2.0-GGUF:Q4_K_M", "20.00", "20GB", "very fast 16B MoE model with 1.4B activated parameters per expert", "InclusionAI", "mit"],
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["qwen3.6:27b-mtp-q4_K_M", "80", "17GB", "Exceptional good 27B model with vision, ranking above GPT-4-turbo, GPT-4.1-nano, GPT-o1, GPT-4o-mini", "Alibaba", "apache-2.0"]
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["frob/qwen3.5-instruct:4b", " 8.52", "4GB", "best model for its size, non-thinking version", "Alibaba", "apache-2.0"],
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["olmo-3:7b-instruct-q4_K_M", " 2.22", "5GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"],
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["qwen3.5:9b-q4_K_M", "18.37", "7GB", "multimodal, outstanding for its size, long-context 256K Tokens, strong instruction following model", "Alibaba", "apache-2.0"],
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["gemma4:12b-it-qat", "47.09", "8GB", "Very mighty, very small model from Google", "Google", "apache-2.0"],
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["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European flagship model, strong multilangual, vision, agentic", "mistral.ai", "apache-2.0"],
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["hf.co/mradermacher/Ling-mini-2.0-GGUF:Q4_K_M", "20.00", "10GB", "very fast 16B MoE model with 1.4B activated parameters per expert", "InclusionAI", "mit"],
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["olmo-3.1:32b-instruct-q4_K_M", "10.25", "19GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"],
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["qwen3.6:27b-mtp-q4_K_M", "70.84", "17GB", "Exceptional good 27B model with vision, ranking above GPT-4-turbo, GPT-4.1-nano, GPT-o1, GPT-4o-mini", "Alibaba", "apache-2.0"],
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["qwen3.6:35b-a3b-mtp-q4_K_M", "63.03", "22GB", "Almost as good as the 27B model, but much faster", "Alibaba", "apache-2.0"]
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];
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const MODEL_TABLE_HEADERS = ["Model", "Ranking", "Size", "Description", "Provider", "License", "Actions"];
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@@ -248,25 +245,55 @@
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}
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}
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async function downloadOllamaModel(hoststub, modelName) {
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async function downloadOllamaModel(hoststub, modelName, onProgress) {
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// stream: true keeps bytes flowing so no idle timeout in the proxy chain
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// kills the connection during long pulls; ollama continues a pull
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// server-side even if this connection drops, so leaving the page is safe
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const response = await fetch(proxyUrl(hoststub, "/api/pull"), {
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method: "POST",
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headers: {"Accept": "application/json", "Content-Type": "application/json"},
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body: JSON.stringify({ model: modelName, stream: false })
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headers: {"Accept": "application/x-ndjson", "Content-Type": "application/json"},
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body: JSON.stringify({ model: modelName, stream: true })
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});
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if (response.status !== 200) {
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const error = new Error(`Failed to download model ${modelName}`);
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error.status = response.status;
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throw error;
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}
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const payload = await response.json().catch(() => null);
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if (payload && payload.error) {
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const error = new Error(payload.error);
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error.status = response.status;
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error.payload = payload;
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throw error;
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// the body is NDJSON: one progress object per line; errors arrive
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// mid-stream as {"error": ...} lines even though the HTTP status is 200
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const reader = response.body.getReader();
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const decoder = new TextDecoder();
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let buffered = "";
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let lastPayload = null;
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const consumeLine = (line) => {
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const trimmed = line.trim();
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if (!trimmed) return;
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let payload = null;
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try {
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payload = JSON.parse(trimmed);
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} catch (parseError) {
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return; // ignore malformed progress lines
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}
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if (payload.error) {
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const error = new Error(payload.error);
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error.status = response.status;
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error.payload = payload;
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throw error;
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}
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lastPayload = payload;
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if (onProgress) onProgress(payload);
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};
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for (;;) {
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const { done, value } = await reader.read();
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if (done) break;
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buffered += decoder.decode(value, { stream: true });
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const lines = buffered.split("\n");
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buffered = lines.pop();
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for (const line of lines) consumeLine(line);
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}
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return payload;
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buffered += decoder.decode();
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consumeLine(buffered);
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return lastPayload;
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}
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async function requestModelsForService(service, hoststub) {
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@@ -482,6 +509,28 @@
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return activityId;
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}
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function updateDownloadActivity(activityId, payload) {
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const wrapper = downloadActivities.get(activityId);
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if (!wrapper || !payload) return;
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const progress = wrapper.querySelector("progress");
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const subtitle = wrapper.querySelector(".download-activity-subtitle");
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const status = payload.status || "";
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if (progress && typeof payload.total === "number" && payload.total > 0
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&& typeof payload.completed === "number") {
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progress.value = Math.min(100, (payload.completed / payload.total) * 100);
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}
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if (subtitle) {
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let text = status || "Download in progress…";
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if (typeof payload.total === "number" && payload.total > 0
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&& typeof payload.completed === "number") {
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const percent = Math.min(100, (payload.completed / payload.total) * 100);
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const mb = (bytes) => (bytes / (1024 * 1024)).toFixed(0);
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text = `${status} — ${percent.toFixed(1)}% (${mb(payload.completed)} / ${mb(payload.total)} MB)`;
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}
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subtitle.textContent = text;
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}
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}
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function removeDownloadActivity(activityId) {
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const wrapper = downloadActivities.get(activityId);
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if (wrapper && wrapper.parentNode) {
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@@ -1749,37 +1798,108 @@
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});
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}
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/*** pending pulls are remembered in localStorage so that a reloaded page
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*** can re-attach to downloads which are still running on the ollama server
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*** (a second /api/pull for the same model joins the running download) ***/
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const PENDING_PULLS_STORAGE_KEY = "yacy.llm.pendingPulls";
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const PENDING_PULL_MAX_AGE_MS = 24 * 60 * 60 * 1000;
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function readPendingPulls() {
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try {
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const parsed = JSON.parse(window.localStorage.getItem(PENDING_PULLS_STORAGE_KEY) || "[]");
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return Array.isArray(parsed) ? parsed : [];
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} catch (storageError) {
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return [];
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}
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}
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function writePendingPulls(entries) {
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try {
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if (entries.length === 0) {
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window.localStorage.removeItem(PENDING_PULLS_STORAGE_KEY);
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} else {
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window.localStorage.setItem(PENDING_PULLS_STORAGE_KEY, JSON.stringify(entries));
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}
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} catch (storageError) {
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// storage unavailable: downloads still work, they just cannot be re-attached
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}
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}
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function addPendingPull(hoststub, modelName) {
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const entries = readPendingPulls().filter(e => !(e.hoststub === hoststub && e.model === modelName));
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entries.push({ hoststub, model: modelName, startedAt: Date.now() });
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writePendingPulls(entries);
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}
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function removePendingPull(hoststub, modelName) {
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writePendingPulls(readPendingPulls().filter(e => !(e.hoststub === hoststub && e.model === modelName)));
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}
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async function performModelDownload(hoststub, modelName, downloadBtn) {
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const activityId = addDownloadActivity(modelName);
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if (downloadBtn) downloadBtn.disabled = true;
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addPendingPull(hoststub, modelName);
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let downloadError = null;
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try {
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await downloadOllamaModel(hoststub, modelName, (payload) => {
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if (activityId) updateDownloadActivity(activityId, payload);
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});
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console.log(`Model ${modelName} is now available on server ${hoststub}.`);
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} catch (err) {
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downloadError = err;
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console.error("Error during model pull request:", err);
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} finally {
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if (downloadBtn) downloadBtn.disabled = false;
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if (activityId) {
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removeDownloadActivity(activityId);
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}
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try {
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await loadModelList();
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} catch (refreshError) {
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console.error("Failed to refresh models after download:", refreshError);
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}
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// a dropped connection is not necessarily a failed pull: ollama keeps
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// pulling server-side, so only report an error if the model is still missing
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const stillMissing = downloadError && !availableModels.includes(modelName);
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const connectionLost = stillMissing && !downloadError.payload
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&& !(typeof downloadError.status === "number" && downloadError.status !== 200);
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if (connectionLost) {
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// keep the pending entry: a page reload will re-attach to the running pull
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alert(`Connection lost while pulling model ${modelName} from server ${hoststub}. The download continues on the server; reload this page to re-attach to its progress.`);
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} else {
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removePendingPull(hoststub, modelName);
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if (stillMissing) {
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const status = typeof downloadError.status === "number" ? downloadError.status : null;
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const message = downloadError.payload
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? `Failed to download model ${modelName}: ${downloadError.message}`
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: `Failed to download model ${modelName}. HTTP status: ${status}`;
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alert(message);
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}
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}
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}
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}
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function resumePendingPulls() {
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const now = Date.now();
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const entries = readPendingPulls().filter(e => e && e.hoststub && e.model
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&& (typeof e.startedAt !== "number" || now - e.startedAt < PENDING_PULL_MAX_AGE_MS));
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writePendingPulls(entries);
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entries.forEach(entry => {
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// re-issuing the pull joins the running download and streams its progress;
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// if the pull already finished it returns success almost immediately
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performModelDownload(entry.hoststub, entry.model, null)
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.catch(resumeError => console.error("Failed to resume model download:", resumeError));
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});
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}
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function createDownloadButton(hoststub, modelName) {
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const downloadBtn = document.createElement("button");
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downloadBtn.type = "button";
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downloadBtn.className = "btn btn-primary btn-sm";
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downloadBtn.textContent = "Download";
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styleActionButton(downloadBtn);
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downloadBtn.addEventListener("click", async () => {
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const activityId = addDownloadActivity(modelName);
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downloadBtn.disabled = true;
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try {
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await downloadOllamaModel(hoststub, modelName);
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console.log(`Model ${modelName} is now available on server ${hoststub}.`);
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} catch (err) {
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const status = err && typeof err.status === "number" ? err.status : null;
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const message = status
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? `Failed to download model ${modelName}. HTTP status: ${status}`
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: `Error pulling model ${modelName} from server ${hoststub}. Check the console for details.`;
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alert(message);
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console.error("Error during model pull request:", err);
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} finally {
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downloadBtn.disabled = false;
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if (activityId) {
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removeDownloadActivity(activityId);
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}
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try {
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await loadModelList();
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} catch (refreshError) {
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console.error("Failed to refresh models after download:", refreshError);
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}
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}
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});
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downloadBtn.addEventListener("click", () => performModelDownload(hoststub, modelName, downloadBtn));
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return downloadBtn;
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}
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@@ -1788,6 +1908,7 @@
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persistedModelCapabilities = readPersistedModelCapabilities();
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normalizeProductionModelRows();
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applyPresetInference();
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resumePendingPulls();
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// auto-show available models if a preset inference exists
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const body = document.body;
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const presetService = (body.getAttribute("data-llm-service") || "").trim();
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