Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OmnipotentFool/Aurvion with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 12,074 Bytes
6b9d0d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 | #pragma once
#include "common.h"
#include "download.h"
#include "preset.h"
#include "server-common.h"
#include "server-http.h"
#include "server-queue.h"
#include <mutex>
#include <condition_variable>
#include <functional>
#include <memory>
#include <set>
/**
* state diagram:
*
* DOWNLOADING βββΊ DOWNLOADED βββΊ (replaced by new instance)
*
* UNLOADED βββΊ LOADING βββΊ LOADED βββββ SLEEPING
* β² β β β²
* ββββfailedββββ β β
* β² βββsleepingββββββ
* βββββββββunloadedββββββββββ
*/
enum server_model_status {
// TODO: also add downloading state when the logic is added
SERVER_MODEL_STATUS_DOWNLOADING,
SERVER_MODEL_STATUS_DOWNLOADED,
SERVER_MODEL_STATUS_UNLOADED,
SERVER_MODEL_STATUS_LOADING,
SERVER_MODEL_STATUS_LOADED,
SERVER_MODEL_STATUS_SLEEPING
};
enum server_model_source {
SERVER_MODEL_SOURCE_PRESET,
SERVER_MODEL_SOURCE_MODELS_DIR,
SERVER_MODEL_SOURCE_CACHE,
};
static std::string server_model_status_to_string(server_model_status status) {
switch (status) {
case SERVER_MODEL_STATUS_DOWNLOADING: return "downloading";
case SERVER_MODEL_STATUS_DOWNLOADED: return "downloaded";
case SERVER_MODEL_STATUS_UNLOADED: return "unloaded";
case SERVER_MODEL_STATUS_LOADING: return "loading";
case SERVER_MODEL_STATUS_LOADED: return "loaded";
case SERVER_MODEL_STATUS_SLEEPING: return "sleeping";
default: return "unknown";
}
}
static std::string server_model_source_to_string(server_model_source source) {
switch (source) {
case SERVER_MODEL_SOURCE_PRESET: return "preset";
case SERVER_MODEL_SOURCE_MODELS_DIR: return "models_dir";
case SERVER_MODEL_SOURCE_CACHE: return "cache";
default: return "unknown";
}
}
struct server_model_meta {
server_model_source source = SERVER_MODEL_SOURCE_CACHE;
common_preset preset;
std::string name;
std::set<std::string> aliases; // additional names that resolve to this model
std::set<std::string> tags; // informational tags, not used for routing
int port = 0;
server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
int64_t last_used = 0; // for LRU unloading
std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
json loaded_info; // info to be reflected via /v1/models endpoint ; if in DOWNLOADING state, it should contain download progress info
json progress; // reflect load or download progress info, if any
int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
mtmd_caps multimodal; // multimodal capabilities
// bool need_download = false; // whether the model needs to be downloaded before loading // TODO @ngxson: implement this
bool is_ready() const {
return status == SERVER_MODEL_STATUS_LOADED;
}
bool is_running() const {
return status == SERVER_MODEL_STATUS_LOADED || status == SERVER_MODEL_STATUS_LOADING || status == SERVER_MODEL_STATUS_SLEEPING;
}
bool is_failed() const {
return status == SERVER_MODEL_STATUS_UNLOADED && exit_code != 0;
}
void update_args(common_preset_context & ctx_presets, std::string bin_path);
void update_caps();
};
struct server_models_routes;
struct server_subproc; // defined in server-models.cpp
struct server_models {
friend struct server_models_routes;
private:
struct instance_t {
std::shared_ptr<server_subproc> subproc; // shared between main thread and monitoring thread
std::thread th;
server_model_meta meta;
FILE * stdin_file = nullptr;
};
std::mutex mutex;
std::condition_variable cv;
std::map<std::string, instance_t> mapping;
// for stopping models
std::condition_variable cv_stop;
std::set<std::string> stopping_models;
// set to true while load_models() is executing a reload; load() will wait until clear
bool is_reloading = false;
// if true, the next get_meta() will trigger a reload of model list
bool need_reload = false;
common_preset_context ctx_preset;
common_params base_params;
std::string bin_path;
std::vector<std::string> base_env;
common_preset base_preset; // base preset from llama-server CLI args
void update_meta(const std::string & name, const server_model_meta & meta);
// unload least recently used models if the limit is reached
void unload_lru();
// not thread-safe, caller must hold mutex
void add_model(server_model_meta && meta);
// notify SSE clients
void notify_sse(const std::string & event, const std::string & model_id, const json & data = nullptr);
public:
server_models(const common_params & params, int argc, char ** argv);
server_response sse; // for real-time updates via SSE endpoint
// (re-)load the list of models from various sources and prepare the metadata mapping
// - if this is called the first time, simply populate the metadata
// - if this is called subsequently (e.g. when refreshing from disk):
// - if a model is running but updated or removed from the source, it will be unloaded
// - if a model is not running, it will be added or updated according to the source
void load_models();
// check if a model instance exists (thread-safe)
bool has_model(const std::string & name);
// return a copy of model metadata (thread-safe)
std::optional<server_model_meta> get_meta(const std::string & name);
// return a copy of all model metadata (thread-safe)
std::vector<server_model_meta> get_all_meta();
// load and unload model instances
// these functions are thread-safe
void load(const std::string & name);
void unload(const std::string & name);
void unload_all();
// download a new model, progress is reported via SSE
// to stop the download, call unload()
void download(common_params_model && model, common_download_opts && opts);
struct update_status_args {
server_model_status status;
int exit_code = 0; // only valid if status == UNLOADED
json loaded_info = nullptr;
json progress = nullptr;
};
// update the status of a model instance (thread-safe)
// also send SSE notification to /models/sse endpoint
void update_status(const std::string & name, const update_status_args & args);
void update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok = true);
// remove a cache model from disk and update the list (thread-safe)
// note: only cache models can be removed; returns false if the model doesn't exist or is not a cache model
bool remove(const std::string & name);
// wait until the model instance is fully loaded (thread-safe)
// note: predicate is called while holding the lock
// return when the model no longer in "loading" state
void wait(const std::string & name, std::function<bool(const server_model_meta &)> predicate);
void wait(std::unique_lock<std::mutex> & lk, const std::string & name, std::function<bool(const server_model_meta &)> predicate);
// ensure the model is in ready state (thread-safe)
// return false if model is ready
// otherwise, load the model and blocking wait until it's ready, then return true (meta may need to be refreshed)
bool ensure_model_ready(const std::string & name);
// proxy an HTTP request to the model instance
server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used);
// handle message sent from server_child::notify_to_router()
// raw input must starts with CMD_CHILD_TO_ROUTER_STATE, followed by a JSON string
// this function is not thread-safe, must be called from instance's monitoring thread
// payload per state:
// state = loading -> payload = {} (TODO: add progress info)
// state = ready -> payload = model_info (json), or {} if wakeup from sleeping
// state = sleeping -> payload = {}
void handle_child_state(const std::string & name, const std::string & raw_input);
};
struct server_child {
// serializes the notify_to_router writes
std::mutex mtx_stdout;
// return true if the current process is a child server instance
bool is_child();
// register the shutdown_handler to be called by the router
// return the monitoring thread (to be joined by the caller)
std::thread setup(const std::function<void(int)> & shutdown_handler);
// notify router server for status changes (e.g. loading, downloading, sleeping, etc.)
// message will be handled by server_models::handle_child_state() on the router side
void notify_to_router(const std::string & state_name, const json & payload);
};
struct server_models_routes {
common_params params;
json ui_settings = json::object(); // Primary: new name
std::atomic<bool> stopping = false; // for graceful disconnecting SSE clients during shutdown
server_models models;
server_models_routes(const common_params & params, int argc, char ** argv)
: params(params), models(params, argc, argv) {
const std::string & cfg = this->params.ui_config_json;
if (!cfg.empty()) {
try {
json json_settings = json::parse(cfg);
ui_settings = json_settings;
} catch (const std::exception & e) {
LOG_ERR("%s: failed to parse UI config: %s\n", __func__, e.what());
throw;
}
}
init_routes();
}
void init_routes();
// handlers using lambda function, so that they can capture `this` without `std::bind`
server_http_context::handler_t get_router_props;
server_http_context::handler_t proxy_get;
server_http_context::handler_t proxy_post;
server_http_context::handler_t get_router_models;
server_http_context::handler_t post_router_models_load;
server_http_context::handler_t post_router_models_unload;
// management API
server_http_context::handler_t get_router_models_sse;
server_http_context::handler_t post_router_models;
server_http_context::handler_t del_router_models;
};
/**
* A simple HTTP proxy that forwards requests to another server
* and relays the responses back.
*/
struct server_http_proxy : server_http_res {
std::function<void()> cleanup = nullptr;
public:
server_http_proxy(const std::string & method,
const std::string & scheme,
const std::string & host,
int port,
const std::string & path,
const std::map<std::string, std::string> & headers,
const std::string & body,
const std::map<std::string, uploaded_file> & files,
const std::function<bool()> should_stop,
int32_t timeout_read,
int32_t timeout_write
);
~server_http_proxy() {
if (cleanup) {
cleanup();
}
}
private:
std::thread thread;
struct msg_t {
std::map<std::string, std::string> headers;
int status = 0;
std::string data;
std::string content_type;
};
};
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