diff options
Diffstat (limited to 'htroot/LLMSelection_p.html')
| -rw-r--r-- | htroot/LLMSelection_p.html | 223 |
1 files changed, 172 insertions, 51 deletions
diff --git a/htroot/LLMSelection_p.html b/htroot/LLMSelection_p.html index c7939b64f..fce0fcc45 100644 --- a/htroot/LLMSelection_p.html +++ b/htroot/LLMSelection_p.html @@ -97,23 +97,20 @@ let persistedModelCapabilities = {}; const RECOMMENDED_MODELS = [ - //["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"], - //["llama3.2:1b-instruct-q4_K_M", " 0.10", "2GB", "A good 1B model", "Meta", "llama3.2"], - ["qwen2.5:1.5b-instruct-q4_K_M", " 0.88", "2GB", "Good small model for 1GB RAM", "Alibaba", "apache-2.0"], - ["llama3.2:3b-instruct-q4_K_M", " 0.66", "3GB", "A good 3B model", "Meta", "llama3.2"], - //["qwen3-vl:2b-instruct-q4_K_M", " 0.73", "3GB", "A very small vision-model, can understand what is sees in images"], + ["qwen2.5:1.5b-instruct-q4_K_M", " 0.88", "1GB", "Good small model for 2GB RAM", "Alibaba", "apache-2.0"], + ["llama3.2:3b-instruct-q4_K_M", " 0.66", "2GB", "A good 3B model", "Meta", "llama3.2"], + ["qwen3:4b-instruct-2507-q4_K_M", " 7.70", "3GB", "a good 4B model", "Alibaba", "apache-2.0"], + ["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"], ["hf.co/unsloth/medgemma-4b-it-GGUF:Q4_K_M", " 0.60", "4GB", "Medical Knowledge and Vision", "Google", "health-ai-developer-foundations"], - ["olmo-3:7b-instruct-q4_K_M", " 2.22", "4GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"], - ["qwen3:4b-instruct-2507-q4_K_M", " 7.70", "3GB", "a good 4B model", "Alibaba", "apache-2.0"], - ["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"], - ["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European flagship model, strong multilangual, vision, agentic", "mistral.ai", "apache-2.0"], - ["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"], - //["phi4:14b-q4_K_M", " 5.24", "10GB", "Strong, made with synthetic data", "Microsoft", "mit"], - //["gemma3:27b-it-q4_K_M", " 4.81", "20GB", "Strong content safety, multilingual support in over 140 languages", "google.com", "gemma"], - //["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"], - ["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"], - ["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"], - ["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"] + ["frob/qwen3.5-instruct:4b", " 8.52", "4GB", "best model for its size, non-thinking version", "Alibaba", "apache-2.0"], + ["olmo-3:7b-instruct-q4_K_M", " 2.22", "5GB", "open and accessible training data, open-source training code", "allenai.org", "apache-2.0"], + ["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"], + ["gemma4:12b-it-qat", "47.09", "8GB", "Very mighty, very small model from Google", "Google", "apache-2.0"], + ["ministral-3:14b-instruct-2512-q4_K_M", " 8.55", "10GB", "European flagship model, strong multilangual, vision, agentic", "mistral.ai", "apache-2.0"], + ["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"], + ["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"], + ["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"], + ["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"] ]; const MODEL_TABLE_HEADERS = ["Model", "Ranking", "Size", "Description", "Provider", "License", "Actions"]; @@ -248,25 +245,55 @@ } } - async function downloadOllamaModel(hoststub, modelName) { + async function downloadOllamaModel(hoststub, modelName, onProgress) { + // stream: true keeps bytes flowing so no idle timeout in the proxy chain + // kills the connection during long pulls; ollama continues a pull + // server-side even if this connection drops, so leaving the page is safe const response = await fetch(proxyUrl(hoststub, "/api/pull"), { method: "POST", - headers: {"Accept": "application/json", "Content-Type": "application/json"}, - body: JSON.stringify({ model: modelName, stream: false }) + headers: {"Accept": "application/x-ndjson", "Content-Type": "application/json"}, + body: JSON.stringify({ model: modelName, stream: true }) }); if (response.status !== 200) { const error = new Error(`Failed to download model ${modelName}`); error.status = response.status; throw error; } - const payload = await response.json().catch(() => null); - if (payload && payload.error) { - const error = new Error(payload.error); - error.status = response.status; - error.payload = payload; - throw error; + // the body is NDJSON: one progress object per line; errors arrive + // mid-stream as {"error": ...} lines even though the HTTP status is 200 + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let buffered = ""; + let lastPayload = null; + const consumeLine = (line) => { + const trimmed = line.trim(); + if (!trimmed) return; + let payload = null; + try { + payload = JSON.parse(trimmed); + } catch (parseError) { + return; // ignore malformed progress lines + } + if (payload.error) { + const error = new Error(payload.error); + error.status = response.status; + error.payload = payload; + throw error; + } + lastPayload = payload; + if (onProgress) onProgress(payload); + }; + for (;;) { + const { done, value } = await reader.read(); + if (done) break; + buffered += decoder.decode(value, { stream: true }); + const lines = buffered.split("\n"); + buffered = lines.pop(); + for (const line of lines) consumeLine(line); } - return payload; + buffered += decoder.decode(); + consumeLine(buffered); + return lastPayload; } async function requestModelsForService(service, hoststub) { @@ -482,6 +509,28 @@ return activityId; } + function updateDownloadActivity(activityId, payload) { + const wrapper = downloadActivities.get(activityId); + if (!wrapper || !payload) return; + const progress = wrapper.querySelector("progress"); + const subtitle = wrapper.querySelector(".download-activity-subtitle"); + const status = payload.status || ""; + if (progress && typeof payload.total === "number" && payload.total > 0 + && typeof payload.completed === "number") { + progress.value = Math.min(100, (payload.completed / payload.total) * 100); + } + if (subtitle) { + let text = status || "Download in progress…"; + if (typeof payload.total === "number" && payload.total > 0 + && typeof payload.completed === "number") { + const percent = Math.min(100, (payload.completed / payload.total) * 100); + const mb = (bytes) => (bytes / (1024 * 1024)).toFixed(0); + text = `${status} — ${percent.toFixed(1)}% (${mb(payload.completed)} / ${mb(payload.total)} MB)`; + } + subtitle.textContent = text; + } + } + function removeDownloadActivity(activityId) { const wrapper = downloadActivities.get(activityId); if (wrapper && wrapper.parentNode) { @@ -1749,37 +1798,108 @@ }); } + /*** pending pulls are remembered in localStorage so that a reloaded page + *** can re-attach to downloads which are still running on the ollama server + *** (a second /api/pull for the same model joins the running download) ***/ + + const PENDING_PULLS_STORAGE_KEY = "yacy.llm.pendingPulls"; + const PENDING_PULL_MAX_AGE_MS = 24 * 60 * 60 * 1000; + + function readPendingPulls() { + try { + const parsed = JSON.parse(window.localStorage.getItem(PENDING_PULLS_STORAGE_KEY) || "[]"); + return Array.isArray(parsed) ? parsed : []; + } catch (storageError) { + return []; + } + } + + function writePendingPulls(entries) { + try { + if (entries.length === 0) { + window.localStorage.removeItem(PENDING_PULLS_STORAGE_KEY); + } else { + window.localStorage.setItem(PENDING_PULLS_STORAGE_KEY, JSON.stringify(entries)); + } + } catch (storageError) { + // storage unavailable: downloads still work, they just cannot be re-attached + } + } + + function addPendingPull(hoststub, modelName) { + const entries = readPendingPulls().filter(e => !(e.hoststub === hoststub && e.model === modelName)); + entries.push({ hoststub, model: modelName, startedAt: Date.now() }); + writePendingPulls(entries); + } + + function removePendingPull(hoststub, modelName) { + writePendingPulls(readPendingPulls().filter(e => !(e.hoststub === hoststub && e.model === modelName))); + } + + async function performModelDownload(hoststub, modelName, downloadBtn) { + const activityId = addDownloadActivity(modelName); + if (downloadBtn) downloadBtn.disabled = true; + addPendingPull(hoststub, modelName); + let downloadError = null; + try { + await downloadOllamaModel(hoststub, modelName, (payload) => { + if (activityId) updateDownloadActivity(activityId, payload); + }); + console.log(`Model ${modelName} is now available on server ${hoststub}.`); + } catch (err) { + downloadError = err; + console.error("Error during model pull request:", err); + } finally { + if (downloadBtn) downloadBtn.disabled = false; + if (activityId) { + removeDownloadActivity(activityId); + } + try { + await loadModelList(); + } catch (refreshError) { + console.error("Failed to refresh models after download:", refreshError); + } + // a dropped connection is not necessarily a failed pull: ollama keeps + // pulling server-side, so only report an error if the model is still missing + const stillMissing = downloadError && !availableModels.includes(modelName); + const connectionLost = stillMissing && !downloadError.payload + && !(typeof downloadError.status === "number" && downloadError.status !== 200); + if (connectionLost) { + // keep the pending entry: a page reload will re-attach to the running pull + 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.`); + } else { + removePendingPull(hoststub, modelName); + if (stillMissing) { + const status = typeof downloadError.status === "number" ? downloadError.status : null; + const message = downloadError.payload + ? `Failed to download model ${modelName}: ${downloadError.message}` + : `Failed to download model ${modelName}. HTTP status: ${status}`; + alert(message); + } + } + } + } + + function resumePendingPulls() { + const now = Date.now(); + const entries = readPendingPulls().filter(e => e && e.hoststub && e.model + && (typeof e.startedAt !== "number" || now - e.startedAt < PENDING_PULL_MAX_AGE_MS)); + writePendingPulls(entries); + entries.forEach(entry => { + // re-issuing the pull joins the running download and streams its progress; + // if the pull already finished it returns success almost immediately + performModelDownload(entry.hoststub, entry.model, null) + .catch(resumeError => console.error("Failed to resume model download:", resumeError)); + }); + } + function createDownloadButton(hoststub, modelName) { const downloadBtn = document.createElement("button"); downloadBtn.type = "button"; downloadBtn.className = "btn btn-primary btn-sm"; downloadBtn.textContent = "Download"; styleActionButton(downloadBtn); - downloadBtn.addEventListener("click", async () => { - const activityId = addDownloadActivity(modelName); - downloadBtn.disabled = true; - try { - await downloadOllamaModel(hoststub, modelName); - console.log(`Model ${modelName} is now available on server ${hoststub}.`); - } catch (err) { - const status = err && typeof err.status === "number" ? err.status : null; - const message = status - ? `Failed to download model ${modelName}. HTTP status: ${status}` - : `Error pulling model ${modelName} from server ${hoststub}. Check the console for details.`; - alert(message); - console.error("Error during model pull request:", err); - } finally { - downloadBtn.disabled = false; - if (activityId) { - removeDownloadActivity(activityId); - } - try { - await loadModelList(); - } catch (refreshError) { - console.error("Failed to refresh models after download:", refreshError); - } - } - }); + downloadBtn.addEventListener("click", () => performModelDownload(hoststub, modelName, downloadBtn)); return downloadBtn; } @@ -1788,6 +1908,7 @@ persistedModelCapabilities = readPersistedModelCapabilities(); normalizeProductionModelRows(); applyPresetInference(); + resumePendingPulls(); // auto-show available models if a preset inference exists const body = document.body; const presetService = (body.getAttribute("data-llm-service") || "").trim(); |