Granite switch decision 256 options

conversion/granite.py

@@ -296,19 +296,20 @@ def _has_decision_adapter(self) -> bool:
     def set_vocab(self):
         super().set_vocab()
         if self._has_decision_adapter():
-            self.gguf_writer.add_chat_template([{"name": "systemone", "template": self._systemone_template()}])
+            systemone_template = self._systemone_template()
+            logger.info("System One Template: %s", systemone_template)
+            self.gguf_writer.add_chat_template([{"name": "systemone", "template": systemone_template}])
 
     def _systemone_template(self) -> str:
         # prompt of the decision aLoRA: a state turn, then a question turn that opens with the control token
         # (it replaces the first token of "### DECISION"), the answer is the label token that comes next
         description = jinja_str_or_json("o.description")
         option = (
-            "{% if type != 'noul' %}{{ letters[loop.index0] }}: {% if o.description %}" + description + "{% endif %}"
+            "{% if type != 'noul' %}{{ o.label }}: {% if o.description %}" + description + "{% endif %}"
             "{% elif o.key == 'true' %}A: {% if o.description %}" + description + "{% else %}The statement is true.{% endif %}"
             "{% else %}B: {% if o.description %}" + description + "{% else %}The statement is false.{% endif %}{% endif %}"
         )
         return (
-            "{% set letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' %}"
             "<|start_of_role|>user<|end_of_role|>State:\n" + jinja_str_or_json("state") + "<|end_of_text|>\n"
             "<|start_of_role|>user<|end_of_role|><|decision|> DECISION\nQuestion:\n" + jinja_str_or_json("instructions")
             + "{% if type == 'score' %} Rate along the ordered levels below (lowest first).{% endif %}"

tools/server/README.md

@@ -1723,7 +1723,7 @@ Follows the [TypeSafe API](https://docs.typesafe.ai/api), streaming is not suppo
 
 The questions of a request are answered independently, an answer does not depend on the other questions. The exception is clef: it reads all the questions in one prompt and decides them jointly.
 
-The number of options of a `choice` question is limited by the model, for example: 52 for openjev and granite-switch, 255 for laya, clef, pplx-decider, vllm-decision2, vllm-d3, lfm2-d1 and lfm2-d1-omni. For laya, long questions and options are truncated to the token budget the model was trained with.
+The number of options of a `choice` question is limited by the model, for example: 52 for openjev, 255 for granite-switch, laya, clef, pplx-decider, vllm-decision2, vllm-d3, lfm2-d1 and lfm2-d1-omni. For laya, long questions and options are truncated to the token budget the model was trained with.
 
 For laya, clef, vllm-d3 and lfm2-d1-omni, the whole prompt is evaluated in one batch: it must fit in `--ubatch-size`. An lfm2-d1-omni prompt is cut to 16384 tokens. For vllm-decision2, the options of a question and the end of the prompt must fit in one batch. A server that runs clef only serves this endpoint, text generation is not available. A decision model that is also a text model, such as granite-switch, serves both and reports `["text", "decisions"]`.
 

tools/server/server-decision.cpp

@@ -87,7 +87,7 @@ void server_decision_context::init(const llama_model * model) {
         bias[level] = std::strtof(val, nullptr);
     }
 
-    if (model_type == COMMON_DECISION_TYPE_OPENJEV || model_type == COMMON_DECISION_TYPE_GRANITE_SWITCH) {
+    if (model_type == COMMON_DECISION_TYPE_OPENJEV) {
         // one letter per option, each must be a single token
         const std::string letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
         for (const char c : letters) {
@@ -99,7 +99,7 @@ void server_decision_context::init(const llama_model * model) {
         }
         n_options_max   = labels.size();
         noul_true_first = true;
-    } else if (model_type == COMMON_DECISION_TYPE_LEV || model_type == COMMON_DECISION_TYPE_NIMBLE || model_type == COMMON_DECISION_TYPE_PPLX_DECIDER || model_type == COMMON_DECISION_TYPE_VLLM_D3) {
+    } else if (model_type == COMMON_DECISION_TYPE_LEV || model_type == COMMON_DECISION_TYPE_GRANITE_SWITCH || model_type == COMMON_DECISION_TYPE_NIMBLE || model_type == COMMON_DECISION_TYPE_PPLX_DECIDER || model_type == COMMON_DECISION_TYPE_VLLM_D3) {
         // label codes are A..Z then AA..ZZ, only the ones that are a single token are used
         std::vector<std::string> codes;
         for (char a = 'A'; a <= 'Z'; a++) {
@@ -121,6 +121,9 @@ void server_decision_context::init(const llama_model * model) {
         if (model_type == COMMON_DECISION_TYPE_VLLM_D3) {
             canonical_legend = true;
         }
+        if (model_type == COMMON_DECISION_TYPE_GRANITE_SWITCH) {
+            noul_true_first = true;
+        }
     } else if (model_type == COMMON_DECISION_TYPE_KEV) {
         // the hidden state of an option is read at the token that ends it
         const auto toks = common_tokenize(vocab, "<|box_end|>", false, true);