fixed bug in ai.production_models default value and replaced some

outdated LLMs with new ones

3 files changed, 17 insertions, 18 deletions

diff --git a/defaults/yacy.init b/defaults/yacy.init
index 90523996d..2274d4d59 100644
--- a/defaults/yacy.init
+++ b/defaults/yacy.init
@@ -1423,7 +1423,7 @@ decoration.hostanalysis = false
 decoration.simpleheadernavbar = navbar-default
 
 # ai settings
-ai.production_models = {}
+ai.production_models = []
 ai.system-prompt = You are a smart and helpful chatbot. If possible, use friendly emojies.
 ai.llm-system-prefix = \n\nYou may receive additional expert knowledge in the user prompt after a 'Additional Information' headline to enhance your knowledge. Use it only if applicable.
 ai.llm-user-prefix = \n\nAdditional Information:\n\nbelow you find a collection of texts that might be useful to generate a response. Do not discuss these documents, just use them to answer the question above.\n\n
diff --git a/htroot/LLMSelection_p.html b/htroot/LLMSelection_p.html
index a175b1b96..312386389 100644
--- a/htroot/LLMSelection_p.html
+++ b/htroot/LLMSelection_p.html
@@ -61,21 +61,19 @@
       let cachedVisionTestImagePromise = null;
 
       const RECOMMENDED_MODELS = [
-        ["smollm2:360m-instruct-q4_K_M",                                                         "0.001", "0.5GB", "english-only minimalistic model for small devices", "Huggingface", "apache-2.0"],
-        ["hf.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF:Q4_K_M",                                 "0.09", "1.5GB", "European Union - funded model, multilingual", "Various European Universities", "apache-2.0"],
-        ["llama3.2:1b-instruct-q4_K_M",                                                          "0.18", "1.5GB", "A good 1B model", "Meta", "llama3.2"],
-        ["llama3.2:3b-instruct-q4_K_M",                                                          "0.66", "3GB", "A good 3B model", "Meta", "llama3.2"],
-        ["qwen3-vl:2b-instruct-q4_K_M",                                                          "",     "3GB", "A very small vision-model, can understand what is sees in images"],
-        ["qwen3:4b-instruct-2507-q4_K_M",                                                        "7.70", "3GB", "Exceptional good 4B model", "Alibaba", "apache-2.0"],
-        ["hf.co/mradermacher/Josiefied-Qwen3-4B-Instruct-2507-abliterated-v1-GGUF:Q4_K_M",       "4.41", "3GB", "Uncensored version of qwen3:4b", "huggingface.co/Goekdeniz-Guelmez", "apache-2.0"],
-        ["hf.co/mradermacher/medgemma-4b-it-GGUF:Q4_K_M",                                        "0.84", "4GB", "Medical Knowledge and Vision", "Google", "health-ai-developer-foundations"],
-        ["hf.co/mradermacher/occiglot-7b-eu5-instruct-GGUF:Q4_K_M",                              "0.19", "5GB", "Support for top-5 EU languages (English, Spanish, French, German, and Italian)", "occiglot.eu", "apache-2.0"],
-        ["hf.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF:Q4_K_M",                                  "0.22", "5GB", "open and accessible training data, open-source training code, very fast", "allenai.org", "apache-2.0"],
-        ["hf.co/bartowski/AGI-0_Art-0-8B-GGUF:Q4_K_M",                                           "11.9", "6GB", "Exceptional good 8B model, ranking above ChatGPT-3.5", "AGI-0.com and Alibaba", "apache-2.0"],
-        ["phi4:14b-q4_K_M",                                                                      "5.24", "10GB", "Very strong, made with synthetic data", "Microsoft", "mit"],
-        ["hf.co/mistralai/Magistral-Small-2509-GGUF:Q4_K_M",                                     "5.18", "16GB", "European flagship model, strong multilangual, reasoning", "mistral.ai", "apache-2.0"],
-        ["hf.co/bartowski/cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF:Q4_K_M", "3.43", "16GB", "Uncensored multilingual european Mistral-24B for role playing", "mistral.ai and dphn.ai", "apache-2.0"],
-        ["gemma3:27b-it-q4_K_M",                                                                 "4.81", "20GB", "Strong content safety, multilingual support in over 140 languages", "google.com", "gemma"],
+        ["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", "1.5GB", "A good 1B model", "Meta", "llama3.2"],
+        ["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"],
+        ["qwen3:4b-instruct-2507-q4_K_M",                                                        "7.70",   "3GB", "Exceptional good 4B model", "Alibaba", "apache-2.0"],
+        ["hf.co/mradermacher/Josiefied-Qwen3-4B-Instruct-2507-abliterated-v1-GGUF:Q4_K_M",       "4.41",   "3GB", "Uncensored version of qwen3:4b", "huggingface.co/Goekdeniz-Guelmez", "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"],
+        ["hf.co/bartowski/AGI-0_Art-0-8B-GGUF:Q4_K_M",                                           "11.9",   "6GB", "Exceptional good 8B model, ranking above ChatGPT-3.5", "AGI-0.com and Alibaba", "apache-2.0"],
+        ["phi4:14b-q4_K_M",                                                                      "5.24",  "10GB", "Very strong, made with synthetic data", "Microsoft", "mit"],
+        ["hf.co/mistralai/Magistral-Small-2509-GGUF:Q4_K_M",                                     "5.18",  "16GB", "European flagship model, strong multilangual, reasoning", "mistral.ai", "apache-2.0"],
+        ["hf.co/bartowski/cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF:Q4_K_M", "3.43",  "16GB", "Uncensored multilingual european Mistral-24B for role playing", "mistral.ai and dphn.ai", "apache-2.0"],
+        ["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"]
       ];
 
@@ -375,7 +373,7 @@
             ranking: info.ranking || "",
             size: info.size || "",
             description: info.description || "",
-            service: info.service || "",
+            provider: info.provider || "",
             license: info.license || "",
             renderActions: () => createAvailableModelActionButtons(service, id)
           });
@@ -397,7 +395,7 @@
             ranking: info.ranking || "",
             size: info.size || "",
             description: info.description || "",
-            service: info.service || "",
+            provider: info.provider || "",
             license: info.license || "",
             renderActions: () => createDownloadButton(hoststub, m[0])
           };
diff --git a/source/net/yacy/htroot/LLMSelection_p.java b/source/net/yacy/htroot/LLMSelection_p.java
index 305864739..68767db04 100644
--- a/source/net/yacy/htroot/LLMSelection_p.java
+++ b/source/net/yacy/htroot/LLMSelection_p.java
@@ -83,6 +83,7 @@ public class LLMSelection_p {
         
         // generate table for production_models
         String pms = sb.getConfig("ai.production_models", "[]");
+        if (pms.isEmpty() || pms.equals("{}")) pms = "[]";
         try {
             production_models = new JSONArray(new JSONTokener(pms));
             for (int i = 0; i < production_models.length(); i++) {