Merge branch 'MCP-SSE-Server-support' of https://github.com/brucepro/llama.cpp into MCP-SSE-Server-support

Author: brucepro

.devops/musa.Dockerfile

@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
+ARG MUSA_VERSION=rc3.1.1
 # Target the MUSA build image
 ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 

.github/workflows/build.yml

@@ -443,7 +443,7 @@ jobs:
 
   ubuntu-22-cmake-musa:
     runs-on: ubuntu-22.04
-    container: mthreads/musa:rc3.1.0-devel-ubuntu22.04
+    container: mthreads/musa:rc3.1.1-devel-ubuntu22.04
 
     steps:
       - name: Clone

README.md

@@ -235,6 +235,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [HIP](docs/build.md#hip) | AMD GPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
+| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
 
 ## Building the project
 
@@ -518,5 +519,18 @@ If your issue is with model generation quality, then please at least scan the fo
     - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
     - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
 
-#### References
-
+## Completions
+Command-line completion is available for some environments.
+
+#### Bash Completion
+```bash
+$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
+$ source ~/.llama-completion.bash
+```
+Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
+automatically. For example:
+```console
+$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
+```
+
+## References

common/arg.cpp

@@ -365,6 +365,108 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
     print_options(specific_options);
 }
 
+static void common_params_print_completion(common_params_context & ctx_arg) {
+    std::vector<common_arg *> common_options;
+    std::vector<common_arg *> sparam_options;
+    std::vector<common_arg *> specific_options;
+
+    for (auto & opt : ctx_arg.options) {
+        if (opt.is_sparam) {
+            sparam_options.push_back(&opt);
+        } else if (opt.in_example(ctx_arg.ex)) {
+            specific_options.push_back(&opt);
+        } else {
+            common_options.push_back(&opt);
+        }
+    }
+
+    printf("_llama_completions() {\n");
+    printf("    local cur prev opts\n");
+    printf("    COMPREPLY=()\n");
+    printf("    cur=\"${COMP_WORDS[COMP_CWORD]}\"\n");
+    printf("    prev=\"${COMP_WORDS[COMP_CWORD-1]}\"\n\n");
+
+    printf("    opts=\"");
+    auto print_options = [](const std::vector<common_arg *> & options) {
+        for (const common_arg * opt : options) {
+            for (const char * arg : opt->args) {
+                printf("%s ", arg);
+            }
+        }
+    };
+
+    print_options(common_options);
+    print_options(sparam_options);
+    print_options(specific_options);
+    printf("\"\n\n");
+
+    printf("    case \"$prev\" in\n");
+    printf("        --model)\n");
+    printf("            COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n");
+    printf("            return 0\n");
+    printf("            ;;\n");
+    printf("        --grammar-file)\n");
+    printf("            COMPREPLY=( $(compgen -f -X '!*.gbnf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n");
+    printf("            return 0\n");
+    printf("            ;;\n");
+    printf("        *)\n");
+    printf("            COMPREPLY=( $(compgen -W \"${opts}\" -- \"$cur\") )\n");
+    printf("            return 0\n");
+    printf("            ;;\n");
+    printf("    esac\n");
+    printf("}\n\n");
+
+    std::set<std::string> executables = {
+        "llama-batched",
+        "llama-batched-bench",
+        "llama-bench",
+        "llama-cli",
+        "llama-convert-llama2c-to-ggml",
+        "llama-cvector-generator",
+        "llama-embedding",
+        "llama-eval-callback",
+        "llama-export-lora",
+        "llama-gbnf-validator",
+        "llama-gen-docs",
+        "llama-gguf",
+        "llama-gguf-hash",
+        "llama-gguf-split",
+        "llama-gritlm",
+        "llama-imatrix",
+        "llama-infill",
+        "llama-llava-cli",
+        "llama-llava-clip-quantize-cli",
+        "llama-lookahead",
+        "llama-lookup",
+        "llama-lookup-create",
+        "llama-lookup-merge",
+        "llama-lookup-stats",
+        "llama-minicpmv-cli",
+        "llama-parallel",
+        "llama-passkey",
+        "llama-perplexity",
+        "llama-q8dot",
+        "llama-quantize",
+        "llama-quantize-stats",
+        "llama-qwen2vl-cli",
+        "llama-retrieval",
+        "llama-run",
+        "llama-save-load-state",
+        "llama-server",
+        "llama-simple",
+        "llama-simple-chat",
+        "llama-speculative",
+        "llama-speculative-simple",
+        "llama-tokenize",
+        "llama-tts",
+        "llama-vdot"
+    };
+
+    for (const auto& exe : executables) {
+        printf("complete -F _llama_completions %s\n", exe.c_str());
+    }
+}
+
 static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & value) {
     std::vector<ggml_backend_dev_t> devices;
     auto dev_names = string_split<std::string>(value, ',');
@@ -426,6 +528,10 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
             }
             exit(0);
         }
+        if (ctx_arg.params.completion) {
+            common_params_print_completion(ctx_arg);
+            exit(0);
+        }
     } catch (const std::invalid_argument & ex) {
         fprintf(stderr, "%s\n", ex.what());
         ctx_arg.params = params_org;
@@ -494,6 +600,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             exit(0);
         }
     ));
+    add_opt(common_arg(
+        {"--completion-bash"},
+        "print source-able bash completion script for llama.cpp",
+        [](common_params & params) {
+            params.completion = true;
+        }
+    ));
     add_opt(common_arg(
         {"--verbose-prompt"},
         string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
@@ -674,7 +787,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--no-context-shift"},
-        string_format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
+        string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
         [](common_params & params) {
             params.ctx_shift = false;
         }
@@ -946,6 +1059,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.min_p = std::stof(value);
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--top-nsigma"}, "N",
+        string_format("top-n-sigma sampling (default: %.1f, -1.0 = disabled)", params.sampling.top_n_sigma),
+        [](common_params & params, const std::string & value) {
+            params.sampling.top_n_sigma = std::stof(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}).set_sparam());
     add_opt(common_arg(
         {"--xtc-probability"}, "N",
         string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability),
@@ -1975,6 +2095,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.use_jinja = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
+    add_opt(common_arg(
+        {"--reasoning-format"}, "FORMAT",
+        "reasoning format (default: deepseek; allowed values: deepseek, none)\n"
+        "controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).\n"
+        "only supported for non-streamed responses",
+        [](common_params & params, const std::string & value) {
+            /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
+            else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
+            else { std::invalid_argument("invalid value"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
@@ -2324,5 +2455,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_TTS}));
 
+    add_opt(common_arg(
+        {"--embd-bge-small-en-default"},
+        string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
+            params.hf_file = "bge-small-en-v1.5-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--embd-e5-small-en-default"},
+        string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
+            params.hf_file = "e5-small-v2-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--embd-gte-small-default"},
+        string_format("use default gte-small model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
+            params.hf_file = "gte-small-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
     return ctx_arg;
 }

common/chat-template.hpp

@@ -249,16 +249,30 @@ class chat_template {
                     inputs.add_generation_prompt = false;
                     full = apply(inputs);
                 }
-
-                if (full.find(prefix) != 0) {
-                    if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
-                        prefix = prefix.substr(0, prefix.size() - eos_token_.size());
+                auto eos_pos_last = full.rfind(eos_token_);
+                if (eos_pos_last == prefix.size() - eos_token_.size() ||
+                      (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) {
+                    full = full.substr(0, eos_pos_last);
+                }
+                size_t common_prefix_length = 0;
+                for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) {
+                    if (prefix[i] != full[i]) {
+                        break;
                     }
+                    if (prefix[i] == '<') {
+                        // DeepSeek R1's template (as of 20250209) adds a trailing <think> if add_generation_prompt,
+                        // but it removes thinking tags for past messages.
+                        // The prefix and full strings diverge at <think> vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <.
+                        continue;
+                    }
+                    common_prefix_length = i + 1;
                 }
-                if (full.find(prefix) != 0) {
+                auto example = full.substr(common_prefix_length);
+                if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) {
                     fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
+                } else {
+                    tool_call_example_ = example;
                 }
-                tool_call_example_ = full.substr(prefix.size());
             }
         } catch (const std::exception & e) {
             fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
@@ -363,7 +377,7 @@ class chat_template {
             if (polyfill_tools) {
                 adjusted_messages = add_system(inputs.messages,
                     "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
-                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
+                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n"));
             } else {
                 adjusted_messages = inputs.messages;
             }