metal: advance the LFRU recency clock on the GPU-prerouted decode path (#417)

On Metal, when routing is precomputed on the GPU (g_pre_idx), the moe fast
path bumps eusage/ehit/eheat for the selected experts but skips the one thing
the full CPU router does at the equivalent site: elast[layer][e] =
++eaccess_clock. So the session-local recency clock advances during prefill
(full router) but freezes the moment GPU-prerouted decode starts, and REPIN's
tier_pick_lfru() tie-breaker then runs on stale recency for the rest of the
run. Mirror the exact update the non-Metal path already does. Inside
#ifdef COLI_METAL, so CPU/CUDA are untouched; elast only feeds the LFRU
eviction heuristic, so this cannot affect output, only which experts REPIN
keeps warm.

Found and reported by @monotophic with a source-level trace repro
(ELAST_TRACE). Fix is inspection-verified against line ~3055; needs a
Metal build to exercise end-to-end.

Closes #417

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.clang-format

@@ -0,0 +1,10 @@
+BasedOnStyle: LLVM
+IndentWidth: 4
+ColumnLimit: 120
+AllowShortFunctionsOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: AllIfsAndElse
+AllowShortLoopsOnASingleLine: true
+BreakBeforeBraces: Attach
+PointerAlignment: Right
+SpaceAfterCStyleCast: false
+SortIncludes: false

.editorconfig

@@ -0,0 +1,24 @@
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+indent_style = space
+indent_size = 4
+
+[*.{c,h,cu}]
+indent_size = 4
+
+[*.{ts,tsx,js,json,css}]
+indent_size = 2
+
+[Makefile]
+indent_style = tab
+
+[*.yml]
+indent_size = 2
+
+[*.md]
+trim_trailing_whitespace = false

.gitignore

@@ -68,3 +68,7 @@ c/tests/test_decode_batch
 c/tests/test_i4_acc512
 c/tests/test_idot
 c/tests/test_uring
+olmoe_merged/
+olmoe_i4/
+c/olmoe_merged/
+c/olmoe_i4/

README.md

@@ -151,6 +151,10 @@ scale-granularity/rotation ablations live in
 
 ## Get started
 
+> **New here?** The [Quick Start guide](docs/quickstart.md) walks through
+> install → build → model → first chat step by step for Linux, Windows, and
+> macOS, with copy-paste commands and no assumed background.
+
 ### 1. Get the model
 
 A pre-converted **GLM-5.2 int4** container is on Hugging Face — **use the
@@ -183,6 +187,10 @@ COLI_MODEL=/nvme/glm52_i4 ./coli doctor   # read-only readiness check
 The engine at runtime is pure C — python is only used by the one-time converter
 and the optional API gateway.
 
+Prefer a `coli` command on your PATH? From a checkout, `pip install -e .`
+registers it (the engine itself still lives in `c/` — this is an editable
+install from the clone, not a standalone wheel).
+
 ### 3. Go deeper
 
 | topic | doc |

c/glm.c

@@ -1336,7 +1336,12 @@ static jval* cfg_root(const char *snap, char **arena){
     char p[2048]; snprintf(p,sizeof(p),"%s/config.json",snap);
     FILE *f=fopen(p,"rb"); if(!f){perror(p);exit(1);}
     fseek(f,0,SEEK_END); long n=ftell(f); fseek(f,0,SEEK_SET);
-    char *b=malloc(n+1); size_t got=fread(b,1,n,f); b[got]=0; fclose(f);
+    /* SEC: config.json arriva dalla dir modello non fidata. Limita la dimensione
+     * (un file ostile enorme = OOM al load) e controlla la malloc: senza il NULL
+     * check, b[got]=0 su malloc fallita era un NULL-deref. */
+    if(n<0 || n>(256L<<20)){ fprintf(stderr,"%s: size %ld out of range (0..256 MB)\n",p,n); exit(1); }
+    char *b=malloc((size_t)n+1); if(!b){ fprintf(stderr,"OOM reading %s (%ld bytes)\n",p,n); exit(1); }
+    size_t got=fread(b,1,(size_t)n,f); b[got]=0; fclose(f);
     if((long)got!=n) fprintf(stderr,"warning: short read on %s (%ld of %ld)\n",p,(long)got,n);
     return json_parse(b,arena);
 }
@@ -1375,8 +1380,9 @@ static void load_cfg(Cfg *c, const char *snap){
       FILE *gf=fopen(gp,"rb");                  /* assente = nessun problema: e' opzionale */
       if(gf){
         fseek(gf,0,SEEK_END); long gn=ftell(gf); fseek(gf,0,SEEK_SET);
-        if(gn>0){
-            char *gb=malloc(gn+1); size_t gg=fread(gb,1,gn,gf); gb[gg]=0;
+        char *gb = (gn>0 && gn<=(256L<<20)) ? malloc((size_t)gn+1) : NULL;   /* SEC: cap + NULL check */
+        if(gb){
+            size_t gg=fread(gb,1,(size_t)gn,gf); gb[gg]=0;
             char *ga=NULL; jval *gr=json_parse(gb,&ga);
             jval *ge=gr?json_get(gr,"eos_token_id"):NULL;
             if(ge){
@@ -1445,6 +1451,27 @@ static int detect_group_size(int O, int I, int64_t ns){
     return 0;
 }
 
+/* SEC: risolve e VALIDA il formato quantizzato di un tensore [O,I] letto da un
+ * container non fidato (mirror). L'inferenza precedente (`?1:?2:3`) cadeva su
+ * int2 per QUALSIASI conteggio byte non riconosciuto: un peso troppo corto
+ * diventava un int2 valido e il matmul leggeva oltre il buffer (O*I nibble a
+ * 4/byte). Qui i byte del peso devono corrispondere a un layout noto e i byte
+ * della scala alla cardinalita' attesa (O per-row, O*ng per-gruppo) — altrimenti
+ * si termina invece di sforare. Ritorna fmt (1/2/3/4) e scrive *gs. */
+static int qt_resolve_fmt(const char *name, int O, int I, int64_t nb, int64_t ns, int *gs){
+    int64_t exp_i8=(int64_t)O*I, exp_i4=(int64_t)O*((I+1)/2), exp_i2=(int64_t)O*((I+3)/4);
+    int fmt = (nb==exp_i8)?1 : (nb==exp_i4)?2 : (nb==exp_i2)?3 : 0;
+    if(!fmt){
+        fprintf(stderr,"%s: quantized weight is %lld bytes — no int8/int4/int2 layout for [%d,%d], refusing (untrusted container)\n",
+                name,(long long)nb,O,I); exit(1); }
+    *gs=0;
+    if(fmt==2){ int g=detect_group_size(O,I,ns); if(g>0){ fmt=4; *gs=g; } }
+    int64_t exp_scale = (fmt==4)? (int64_t)O*((I+*gs-1)/(*gs)) : (int64_t)O;   /* in FLOAT */
+    if(ns != exp_scale*4){
+        fprintf(stderr,"%s: scale array is %lld bytes — expected %lld for [%d,%d] fmt=%d, refusing (untrusted container)\n",
+                name,(long long)ns,(long long)(exp_scale*4),O,I,fmt); exit(1); }
+    return fmt;
+}
 /* costruisce un QT [O,I] dal disco in `t` (buffer riusabili tra chiamate).
  *  - se esiste `name.qs`: pesi GIA' quantizzati nel container (U8 qdata + F32 scala) -> letti diretti
  *  - altrimenti: tensore pieno (f32/bf16) -> quantizzato a runtime a `bits` (oracolo tiny / pesi pieni)
@@ -1454,12 +1481,11 @@ static void qt_from_disk(Model *m, const char *name, int O, int I, int bits, int
     if(st_has(&m->S,sn)){
         int64_t nb=st_nbytes(&m->S,name);
         int64_t ns=st_nbytes(&m->S,sn);   /* scale bytes (F32) */
-        /* Detect int4-grouped (fmt=4): packed int4 weight bytes BUT scale array is
-         * larger than O*4 — the group size is derived from the scale-array size. */
-        int fmt = (nb==(int64_t)O*I)?1 : (nb==(int64_t)O*((I+1)/2))?2 : 3;
+        /* fmt=4 int4-grouped: byte int4 ma scala > O*4 — gs deriva dalla scala.
+         * qt_resolve_fmt valida entrambi i conteggi contro [O,I] e termina se
+         * non fidati (SEC). */
         int gs=0;
-        if(fmt==2) gs=detect_group_size(O,I,ns);
-        if(gs>0) fmt=4;
+        int fmt = qt_resolve_fmt(name,O,I,nb,ns,&gs);
         if(fmt==1){ if(t->fmt!=1||!t->q8){ t->fmt=1; t->O=O; t->I=I; t->gs=0; t->q8=qalloc(nb); t->s=qsalloc(O); } st_read_raw(&m->S,name,t->q8,drop); }
         else if(fmt==4){ int ng=(I+gs-1)/gs;
             if(t->fmt!=4||!t->q4){ t->fmt=4; t->O=O; t->I=I; t->gs=gs; t->q4=qalloc(nb); t->s=falloc((int64_t)O*ng); }
@@ -1802,11 +1828,8 @@ static int expert_load_impl(Model *m, int layer, int eid, ESlot *s, int fatal){
             QT *qt[3]={&s->g,&s->u,&s->d}; int OO[3]={I,I,D}, II[3]={D,D,I};
             for(int k=0;k<3;k++){
                 int64_t nb=tw[k]->nbytes;
-                int fmt=(nb==(int64_t)OO[k]*II[k])?1:(nb==(int64_t)OO[k]*((II[k]+1)/2))?2:3;
-                /* detect grouped int4 (fmt=4): int4 weight bytes + larger scale array */
                 int gs=0;
-                if(fmt==2) gs=detect_group_size(OO[k],II[k],tq[k]->nbytes);
-                if(gs>0) fmt=4;
+                int fmt=qt_resolve_fmt(tw[k]->name,OO[k],II[k],nb,tq[k]->nbytes,&gs);
                 qt[k]->fmt=fmt; qt[k]->O=OO[k]; qt[k]->I=II[k]; qt[k]->gs=gs; qt[k]->qf=NULL;
                 qt[k]->q8=(int8_t*)((char*)bw[k]+tw[k]->off); qt[k]->q4=(uint8_t*)((char*)bw[k]+tw[k]->off);
                 qt[k]->s=(float*)((char*)bq[k]+tq[k]->off);
@@ -1931,10 +1954,8 @@ static int expert_load_impl(Model *m, int layer, int eid, ESlot *s, int fatal){
     QT *qt[3]={&s->g,&s->u,&s->d}; int OO[3]={I,I,D}, II[3]={D,D,I};
     for(int k=0;k<3;k++){
         int64_t nb=tw[k]->nbytes;
-        int fmt = (nb==(int64_t)OO[k]*II[k])?1 : (nb==(int64_t)OO[k]*((II[k]+1)/2))?2 : 3;
         int gs=0;
-        if(fmt==2) gs=detect_group_size(OO[k],II[k],tq[k]->nbytes);
-        if(gs>0) fmt=4;
+        int fmt=qt_resolve_fmt(tw[k]->name,OO[k],II[k],nb,tq[k]->nbytes,&gs);
         qt[k]->fmt=fmt; qt[k]->O=OO[k]; qt[k]->I=II[k]; qt[k]->gs=gs; qt[k]->qf=NULL;
         qt[k]->q8=(int8_t*)(s->slab+pos[k]); qt[k]->q4=s->slab+pos[k]; qt[k]->s=fp[k];
     }
@@ -2910,6 +2931,11 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int
                 m->eusage[layer][idxs[(int64_t)s*K+kk]]++;
                 ehit_mark(m,layer,idxs[(int64_t)s*K+kk]);
                 if(m->eheat[layer][idxs[(int64_t)s*K+kk]]<UINT32_MAX) m->eheat[layer][idxs[(int64_t)s*K+kk]]++;
+                /* #417: la scorciatoia GPU-prerouted deve far avanzare l'orologio di recency
+                 * come il percorso router completo (riga ~3055), altrimenti elast/eaccess_clock
+                 * si congelano a fine prefill e il tie-breaker LFRU di REPIN gira su punteggi
+                 * stantii durante il decode su Metal. */
+                m->elast[layer][idxs[(int64_t)s*K+kk]]=++m->eaccess_clock;
             }
             for(int d=0;d<D;d++) out[(int64_t)s*D+d]=0;
         }

c/olmoe.c

@@ -5,6 +5,15 @@
  * Densa (embed, attn, router, norme, lm_head) residente in RAM (float32).
  * Expert letti dal disco on-demand via pread+fadvise(DONTNEED), cache LRU per-layer.
  * Matmul multi-thread con OpenMP (niente BLAS).
+ *
+ * ENV VARS:
+ *   PILOT=0/1/2/3 : 0=no prefetch, 1=1-layer lookahead, 2=2-layer, 3=3-layer lookahead
+ *   HOT=N         : pin top-N hot experts per layer permanently (never evict)
+ *   WARMUP=N      : tokens before hot pinning activates (default 5)
+ *   WIDE=N        : prefetch top-K*N candidates (default 1, try 2 or 3)
+ *   SMOOTH=F      : EMA coefficient for routing momentum (default 0.3, range 0.0-0.95)
+ *   CONF_LIMIT=F  : cumulative gate probability threshold for prefetch cutoff (default 0.92)
+ *   (expert queue is sorted by eid for SSD read locality)
  */
 #define _GNU_SOURCE
 #include <stdio.h>
@@ -12,11 +21,22 @@
 #include <string.h>
 #include <math.h>
 #include <time.h>
+#include <pthread.h>
 #if defined(__APPLE__) || defined(__linux__) || defined(__FreeBSD__)
 #include <sys/resource.h>
+#include <unistd.h>
 #endif
 #include "st.h"
 
+#ifdef _WIN32
+#include <windows.h>
+#define sleep_ms(ms) Sleep(ms)
+#else
+#define sleep_ms(ms) usleep((ms) * 1000)
+#endif
+
+
+
 /* ---------- config ---------- */
 typedef struct {
     int hidden, n_layers, n_heads, n_kv_heads, head_dim;
@@ -33,22 +53,61 @@ typedef struct {
  * Ogni weight [out,in] tenuto come int8 (per-riga) + scala float per riga.
  * Cosi' la RAM-cache scende da 4 byte/param (f32) a 1 byte/param: e' il
  * meccanismo che fa stare GLM-5.2 nei 15 GB. dequant-on-use nel matmul. */
-typedef struct { int eid; int8_t *g, *u, *d; float *gs, *us, *ds; uint64_t used; } Slot;
+/* pinned=1 means this slot is strongly preferred to keep (hot expert); it will
+ * not be evicted during normal LRU eviction, but may be displaced under extreme
+ * cache pressure when all slots are pinned or in-flight. */
+typedef struct { int eid; int pinned; int8_t *g, *u, *d; float *gs, *us, *ds; uint64_t used; } Slot;
 typedef struct { Slot *slots; int n, cap; } LCache;
 
 typedef struct {
     Cfg c;
     shards S;
-    int quant_bits;        /* bit di quantizzazione degli expert (2..8); storage int8, niente f32 (#134) */
+    int quant_bits;
     float *embed, *lm_head, *final_norm;
     Layer *L;
     LCache *cache;          /* [n_layers] */
     uint64_t clock, hits, miss;
-    /* kv-cache per-layer: K,V come [H * maxT * head_dim] */
     float **K, **V; int kv_len, max_t;
     double dense_load_s;
+    /* IMPROVEMENT 2: expert frequency heatmap */
+    uint32_t *freq;
+    int freq_token_count, hot_pinned, hot_n, warmup_tokens;
+    int token_count;
+    /* PREDICTION IMPROVEMENT A: per-layer EMA of gate logits across tokens.
+     * momentum_logits[l*E .. (l+1)*E-1] = EMA of gate outputs for layer l.
+     * Used exclusively by the PILOT prefetcher to stabilise routing predictions
+     * across tokens; does NOT affect actual MoE routing (pr is unchanged). */
+    float *momentum_logits; /* [n_layers * n_experts], EMA of gate logits */
+    float pilot_smooth;     /* SMOOTH env: EMA coefficient 0.0-0.9 (default 0.3) */
+    uint8_t *is_pinned;     /* [n_layers * n_experts], 1 if expert is globally pinned */
+    uint8_t *is_queued;     /* [n_layers * n_experts], 1 if expert is currently in the prefetch queue */
+    float pilot_conf_limit; /* CONF_LIMIT env: cumulative gate probability threshold (e.g. 0.92) */
 } Model;
 
+static pthread_mutex_t g_pilot_mx = PTHREAD_MUTEX_INITIALIZER;
+static struct { int l, e; } pilot_q[4096];
+static volatile unsigned pilot_r = 0, pilot_w = 0;
+static Model *pilot_m = NULL;
+static int g_pilot = 0;
+static int g_wide  = 1;  /* IMPROVEMENT 4: top-K * g_wide candidates prefetched */
+
+static void pilot_prefetch(Model *m, int lnext, const float *x, int S);
+static void *pilot_worker(void *arg);
+static void ensure_pilot_worker_started(Model *m);
+static void slot_ensure_allocated(Model *m, Slot *s);
+
+static void ensure_pilot_worker_started(Model *m) {
+    if (!pilot_m) {
+        pilot_m = m;
+        pthread_t t;
+        if (pthread_create(&t, NULL, pilot_worker, NULL) != 0) {
+            fprintf(stderr, "Error: Failed to create pilot prefetch worker thread\n");
+            exit(1);
+        }
+        pthread_detach(t);
+    }
+}
+
 /* ---------- utility ---------- */
 static double now_s(void) { struct timespec t; clock_gettime(CLOCK_MONOTONIC, &t); return t.tv_sec + t.tv_nsec*1e-9; }
 #if defined(__APPLE__)
@@ -210,51 +269,224 @@ static void model_init(Model *m, const char *snap, int cap, int bits) {
         #undef LD
     }
     m->cache = calloc(c->n_layers, sizeof(LCache));
-    for (int i = 0; i < c->n_layers; i++) { m->cache[i].cap = cap; m->cache[i].slots = calloc(cap, sizeof(Slot)); }
+    for (int i = 0; i < c->n_layers; i++) {
+        m->cache[i].cap = cap;
+        m->cache[i].slots = calloc(cap, sizeof(Slot));
+    }
+    /* IMPROVEMENT 2: frequency heatmap for hot expert pinning */
+    m->freq = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint32_t));
+    m->hot_pinned = 0; m->freq_token_count = 0;
+    m->hot_n         = getenv("HOT")    ? atoi(getenv("HOT"))    : 0;
+    m->warmup_tokens = getenv("WARMUP") ? atoi(getenv("WARMUP")) : 5;
+    m->token_count = 0;
+    /* PREDICTION A: routing momentum — EMA of gate logits across tokens.
+     * Initialized to zero; first token sets EMA = fresh logits. */
+    m->momentum_logits = calloc((size_t)c->n_layers * c->n_experts, sizeof(float));
+    float sv = getenv("SMOOTH") ? (float)atof(getenv("SMOOTH")) : 0.3f;
+    if (sv < 0.f) sv = 0.f; if (sv > 0.95f) sv = 0.95f;
+    m->pilot_smooth = sv;
+    m->is_pinned = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint8_t));
+    m->is_queued = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint8_t));
+    float cl = getenv("CONF_LIMIT") ? (float)atof(getenv("CONF_LIMIT")) : 0.92f;
+    if (cl < 0.1f) cl = 0.1f; if (cl > 1.0f) cl = 1.0f;
+    m->pilot_conf_limit = cl;
     m->dense_load_s = now_s() - t0;
+
+    // Persistent Hot Pinning: try to load hot_pinned.bin
+    char pinpath[512];
+    snprintf(pinpath, sizeof(pinpath), "%s/hot_pinned.bin", snap);
+    FILE *pinf = fopen(pinpath, "rb");
+    if (pinf) {
+        size_t expected_size = (size_t)c->n_layers * c->n_experts;
+        if (fread(m->is_pinned, 1, expected_size, pinf) == expected_size) {
+            m->hot_pinned = 1;
+            printf("[HOT] Loaded persistent pinning from %s\n", pinpath);
+            
+            if (g_pilot) {
+                ensure_pilot_worker_started(m);
+                for (int l = 0; l < c->n_layers; l++) {
+                    for (int e = 0; e < c->n_experts; e++) {
+                        if (m->is_pinned[l * c->n_experts + e]) {
+                            unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
+                            unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
+                            if (w - r < 4096) {
+                                pilot_q[w & 4095].l = l; pilot_q[w & 4095].e = e;
+                                pthread_mutex_lock(&g_pilot_mx);
+                                m->is_queued[l * c->n_experts + e] = 1;
+                                pthread_mutex_unlock(&g_pilot_mx);
+                                __atomic_store_n(&pilot_w, w + 1, __ATOMIC_RELEASE);
+                            }
+                        }
+                    }
+                }
+                printf("[HOT] Pre-loading pinned experts into cache...\n");
+                double t_wait = now_s();
+                while (1) {
+                    unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
+                    unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_ACQUIRE);
+                    if (r == w) break;
+                    sleep_ms(2);
+                }
+                printf("[HOT] Pre-loaded in %.1fs!\n", now_s() - t_wait);
+            }
+        }
+        fclose(pinf);
+    }
 }
 
-/* legge un weight dal disco (streaming) e lo quantizza in q[O,I]+scale[O].
- * Container pre-quantizzato (convert_olmoe.py: int8 + scale f32 in "name.qs"):
- * lettura raw diretta — meta' I/O e zero quantize_rows a runtime. Prima di
- * questa patch il container int8 causava SIGBUS (st_read_f32 su tensori I8). */
-static void load_expert_w(Model *m, const char *name, int8_t *q, float *scale, int O, int I, float *tmp) {
-    st_tensor *t = st_find(&m->S, name);
-    if (t && t->dtype == 3) {                    /* I8/U8: container colibri */
-        char qs[300]; snprintf(qs, sizeof(qs), "%s.qs", name);
-        st_read_raw(&m->S, name, q, 1);
-        st_read_f32(&m->S, qs, scale, 1);
-        return;
+static void slot_ensure_allocated(Model *m, Slot *s) {
+    if (s->g) return;
+    Cfg *c = &m->c;
+    int64_t ng = (int64_t)c->inter * c->hidden;
+    int64_t nd = (int64_t)c->hidden * c->inter;
+    int8_t *w_block = malloc(ng + ng + nd);
+    if (!w_block) {
+        fprintf(stderr, "Error: Out of memory allocating slot weights block\n");
+        exit(1);
     }
-    st_read_f32(&m->S, name, tmp, 1);            /* pread + fadvise DONTNEED */
-    quantize_rows(tmp, q, scale, O, I, m->quant_bits);
+    s->g = w_block;
+    s->u = w_block + ng;
+    s->d = w_block + ng + ng;
+    float *s_block = falloc(c->inter + c->inter + c->hidden);
+    s->gs = s_block;
+    s->us = s_block + c->inter;
+    s->ds = s_block + c->inter + c->inter;
+    s->pinned = 0;
+}
+
+static void load_expert_merged(Model *m, int layer, int eid, Slot *s) {
+    char nm[256], qsnm[256];
+    snprintf(nm, sizeof(nm), "model.layers.%d.mlp.experts.%d.merged_weight", layer, eid);
+    snprintf(qsnm, sizeof(qsnm), "model.layers.%d.mlp.experts.%d.qs", layer, eid);
+    st_read_raw(&m->S, nm, s->g, 1);
+    st_read_f32(&m->S, qsnm, s->gs, 0);  /* scales are F32; use typed reader for dtype safety */
 }
 
 /* ---------- cache expert: ritorna i pesi quantizzati (q+scale) da cache o disco ---------- */
 static void expert_get(Model *m, int layer, int eid, Slot **out) {
     LCache *lc = &m->cache[layer];
+    pthread_mutex_lock(&g_pilot_mx);
     for (int i = 0; i < lc->n; i++) if (lc->slots[i].eid == eid) {
-        m->hits++; lc->slots[i].used = ++m->clock; *out = &lc->slots[i]; return;
+        m->hits++; lc->slots[i].used = ++m->clock; *out = &lc->slots[i];
+        pthread_mutex_unlock(&g_pilot_mx);
+        return;
     }
     m->miss++;
     Cfg *c = &m->c;
-    int64_t ng = (int64_t)c->inter * c->hidden, nd = (int64_t)c->hidden * c->inter;
     Slot *s;
     if (lc->n < lc->cap) {
         s = &lc->slots[lc->n++];
-        s->g = malloc(ng); s->u = malloc(ng); s->d = malloc(nd);
-        s->gs = falloc(c->inter); s->us = falloc(c->inter); s->ds = falloc(c->hidden);
-    } else { int lru = 0; for (int i = 1; i < lc->n; i++) if (lc->slots[i].used < lc->slots[lru].used) lru = i; s = &lc->slots[lru]; }
-    float *tmp = falloc(ng > nd ? ng : nd);
-    char nm[256];
-    snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.gate_proj.weight",layer,eid); load_expert_w(m,nm,s->g,s->gs,c->inter,c->hidden,tmp);
-    snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.up_proj.weight",  layer,eid); load_expert_w(m,nm,s->u,s->us,c->inter,c->hidden,tmp);
-    snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.down_proj.weight",layer,eid); load_expert_w(m,nm,s->d,s->ds,c->hidden,c->inter,tmp);
-    free(tmp);
-    s->eid = eid; s->used = ++m->clock;
+        slot_ensure_allocated(m, s);
+    } else {
+        /* LRU eviction — skip pinned and in-flight (eid==-1) slots */
+        int lru = -1;
+        for (int i = 0; i < lc->n; i++) {
+            if (lc->slots[i].pinned || lc->slots[i].eid < 0) continue;
+            if (lru < 0 || lc->slots[i].used < lc->slots[lru].used) lru = i;
+        }
+        if (lru < 0) {
+            /* All slots are pinned or in-flight; find oldest non-in-flight slot
+             * (may be pinned, but never select one currently being loaded). */
+            for (int i = 0; i < lc->n; i++) {
+                if (lc->slots[i].eid < 0) continue; /* never evict in-flight */
+                if (lru < 0 || lc->slots[i].used < lc->slots[lru].used) lru = i;
+            }
+        }
+        if (lru < 0) lru = 0; /* absolute last resort: all in-flight, evict slot 0 */
+        s = &lc->slots[lru];
+        s->pinned = 0;
+    }
+    s->eid = -1;
+    s->used = ++m->clock;
+    pthread_mutex_unlock(&g_pilot_mx);
+
+    load_expert_merged(m, layer, eid, s);
+
+    pthread_mutex_lock(&g_pilot_mx);
+    s->eid = eid;
+    s->pinned = m->is_pinned[layer * c->n_experts + eid];
+    s->used = ++m->clock;
     *out = s;
+    pthread_mutex_unlock(&g_pilot_mx);
 }
 
+/* ---------- IMPROVEMENT 2: pin top-N hot experts per layer ---------- */
+static void pin_hot_experts(Model *m) {
+    Cfg *c = &m->c;
+    if (m->hot_n <= 0 || m->hot_pinned) return;
+    m->hot_pinned = 1;
+    
+    int is_dynamic = (m->hot_n >= 100);
+    double thresh = is_dynamic ? (double)m->hot_n / 1000.0 : 0.0;
+    
+    int pinned_total = 0;
+    for (int l = 0; l < c->n_layers; l++) {
+        uint32_t *freq_l = m->freq + (int64_t)l * c->n_experts;
+        
+        uint64_t layer_total = 0;
+        for (int e = 0; e < c->n_experts; e++) layer_total += freq_l[e];
+        if (layer_total == 0) continue;
+        
+        int max_pin = m->cache[l].cap - 8;
+        if (max_pin < 4) max_pin = 4;
+        
+        int hn = is_dynamic ? max_pin : (m->hot_n < c->n_experts ? m->hot_n : c->n_experts);
+        if (hn > 256) hn = 256;
+        int hot_eids[256];
+        int actual_hn = 0;
+        
+        for (int k = 0; k < hn; k++) {
+            int best = -1; uint32_t bv = 0;
+            for (int e = 0; e < c->n_experts; e++) {
+                int already = 0;
+                for (int j = 0; j < k; j++) if (hot_eids[j] == e) { already = 1; break; }
+                if (!already && freq_l[e] > bv) { bv = freq_l[e]; best = e; }
+            }
+            if (best < 0 || bv == 0) break;
+            if (is_dynamic && bv < thresh * layer_total) break;
+            hot_eids[k] = best;
+            actual_hn++;
+        }
+        
+        for (int k = 0; k < actual_hn; k++) {
+            int eid = hot_eids[k];
+            m->is_pinned[l * c->n_experts + eid] = 1;
+
+            LCache *lc = &m->cache[l];
+            int found = 0;
+            pthread_mutex_lock(&g_pilot_mx);
+            for (int i = 0; i < lc->n; i++) {
+                if (lc->slots[i].eid == eid) { lc->slots[i].pinned = 1; found = 1; break; }
+            }
+            pthread_mutex_unlock(&g_pilot_mx);
+            if (!found && g_pilot > 0) {
+                /* Only enqueue when the prefetch worker is active (PILOT>0). */
+                ensure_pilot_worker_started(m);
+                unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
+                unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
+                int gidx = l * c->n_experts + eid;
+                pthread_mutex_lock(&g_pilot_mx);
+                int already = m->is_queued[gidx];
+                if (!already && w - r < 4096) {
+                    pilot_q[w & 4095].l = l; pilot_q[w & 4095].e = eid;
+                    m->is_queued[gidx] = 1;
+                    __atomic_store_n(&pilot_w, w + 1, __ATOMIC_RELEASE);
+                }
+                pthread_mutex_unlock(&g_pilot_mx);
+            }
+            pinned_total++;
+        }
+    }
+    if (is_dynamic) {
+        printf("[HOT] Dynamic Pinned %d experts total (thresh=%.1f%%) after %d warmup tokens\n",
+               pinned_total, thresh * 100.0, m->freq_token_count);
+    } else {
+        printf("[HOT] Pinned %d experts (top-%d/layer) after %d warmup tokens\n",
+               pinned_total, m->hot_n, m->freq_token_count);
+    }
+}
+
+
 /* ---------- RoPE su un vettore di una testa (head_dim) a posizione assoluta pos ---------- */
 static void rope_head(float *x, int pos, const Cfg *c) {
     int h = c->head_dim / 2;
@@ -325,6 +557,19 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) {
     float *g = falloc(I), *u = falloc(I), *hh = falloc(D);
     for (int s = 0; s < S; s++) {
         float *pr = logits + (int64_t)s*E;
+        if (m->momentum_logits && m->pilot_smooth > 0.f) {
+            float *ema = m->momentum_logits + (int64_t)layer * E;
+            int is_zero = 1;
+            for (int e = 0; e < E; e++) { if (ema[e] != 0.f) { is_zero = 0; break; } }
+            if (is_zero) {
+                for (int e = 0; e < E; e++) ema[e] = pr[e];
+            } else {
+                for (int e = 0; e < E; e++) {
+                    ema[e] = (1.f - m->pilot_smooth) * pr[e] + m->pilot_smooth * ema[e];
+                }
+            }
+        }
+
         softmax_row(pr, E);
         /* top-K indici (selezione parziale) */
         int idx[64]; float val[64];
@@ -337,6 +582,11 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) {
             idx[kk] = best; val[kk] = bv;
         }
         if (c->norm_topk) { float sm=0; for(int kk=0;kk<K;kk++) sm+=val[kk]; for(int kk=0;kk<K;kk++) val[kk]/=sm; }
+        /* IMPROVEMENT 2: update activation heatmap (before pinning activates) */
+        if (!m->hot_pinned && m->freq) {
+            uint32_t *freq_l = m->freq + (int64_t)layer * E;
+            for (int kk = 0; kk < K; kk++) if (idx[kk] >= 0) freq_l[idx[kk]]++;
+        }
         const float *xs = x + (int64_t)s*D;
         for (int kk = 0; kk < K; kk++) {
             Slot *e; expert_get(m, layer, idx[kk], &e);
@@ -352,9 +602,20 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) {
     free(logits); free(g); free(u); free(hh);
 }
 
-/* un passo: token nuovi ids[S] a posizione pos_base. Ritorna logits dell'ultimo token (malloc'd). */
 static float *step(Model *m, const int *ids, int S, int pos_base) {
     Cfg *c = &m->c; int D = c->hidden;
+    if (g_pilot && m->token_count > 0) {
+        /* Flush stale prefetch requests: clear is_queued so pilot_realload
+         * will skip any entries still sitting in pilot_q for the previous
+         * token.  We deliberately do NOT move pilot_w backwards; that would
+         * break the ring-buffer invariant (pilot_r could exceed pilot_w if
+         * the worker consumed an entry concurrently).  The worker will drain
+         * the stale slots harmlessly because pilot_realload already exits
+         * early when the expert is already cached or is_queued is clear. */
+        pthread_mutex_lock(&g_pilot_mx);
+        memset(m->is_queued, 0, (size_t)c->n_layers * c->n_experts);
+        pthread_mutex_unlock(&g_pilot_mx);
+    }
     float *x = falloc((int64_t)S*D);
     for (int s = 0; s < S; s++) memcpy(x + (int64_t)s*D, m->embed + (int64_t)ids[s]*D, D*sizeof(float));
     float *nrm = falloc((int64_t)S*D), *tmp = falloc((int64_t)S*D);
@@ -363,12 +624,26 @@ static float *step(Model *m, const int *ids, int S, int pos_base) {
         for (int s = 0; s < S; s++) rmsnorm_row(nrm + (int64_t)s*D, x + (int64_t)s*D, l->in_ln, D, c->eps);
         attention(m, l, i, nrm, S, pos_base, tmp);
         for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j];
+        /* IMPROVEMENT 1: PILOT=1 -> 1-layer lookahead */
+        if (g_pilot >= 1 && S <= 8 && i + 1 < c->n_layers)
+            pilot_prefetch(m, i + 1, x, S);
         for (int s = 0; s < S; s++) rmsnorm_row(nrm + (int64_t)s*D, x + (int64_t)s*D, l->post_ln, D, c->eps);
         moe(m, l, i, nrm, S, tmp);
         for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j];
+
+        /* PREDICTION IMPROVEMENT C (Residual gate trick):
+         * PILOT=2 -> prefetch layer i+2 using completed state x (containing MoE residual). */
+        if (g_pilot >= 2 && S <= 8 && i + 2 < c->n_layers)
+            pilot_prefetch(m, i + 2, x, S);
+        if (g_pilot >= 3 && S <= 8 && i + 3 < c->n_layers)
+            pilot_prefetch(m, i + 3, x, S);
+        
     }
+    /* count actual tokens processed (S>1 during prefill) */
+    m->token_count += S; m->freq_token_count += S;
+    if (!m->hot_pinned && m->hot_n > 0 && m->freq_token_count >= m->warmup_tokens)
+        pin_hot_experts(m);
     m->kv_len = pos_base + S;
-    /* solo l'ultimo token -> logits */
     float *last = falloc(D);
     rmsnorm_row(last, x + (int64_t)(S-1)*D, m->final_norm, D, c->eps);
     float *logit = falloc(c->vocab);
@@ -377,6 +652,192 @@ static float *step(Model *m, const int *ids, int S, int pos_base) {
     return logit;
 }
 
+static void pilot_realload(Model *m, int layer, int eid) {
+    LCache *lc = &m->cache[layer];
+    Cfg *c = &m->c;
+
+    pthread_mutex_lock(&g_pilot_mx);
+    /* Early-exit if entry was flushed (is_queued cleared) while waiting. */
+    if (!m->is_queued[layer * c->n_experts + eid]) {
+        pthread_mutex_unlock(&g_pilot_mx);
+        return;
+    }
+    for (int i = 0; i < lc->n; i++) {
+        if (lc->slots[i].eid == eid) {
+            m->is_queued[layer * c->n_experts + eid] = 0;
+            pthread_mutex_unlock(&g_pilot_mx);
+            return;
+        }
+    }
+    Slot *s;
+    if (lc->n < lc->cap) {
+        s = &lc->slots[lc->n++];
+        slot_ensure_allocated(m, s);
+    } else {
+        /* LRU eviction — skip pinned and in-flight (eid==-1) slots */
+        int lru = -1;
+        for (int i = 0; i < lc->n; i++) {
+            if (lc->slots[i].pinned || lc->slots[i].eid < 0) continue;
+            if (lru < 0 || lc->slots[i].used < lc->slots[lru].used) lru = i;
+        }
+        if (lru < 0) {
+            m->is_queued[layer * c->n_experts + eid] = 0;
+            pthread_mutex_unlock(&g_pilot_mx);
+            return; /* all pinned/in-flight, skip */
+        }
+        s = &lc->slots[lru]; s->pinned = 0;
+    }
+    s->eid = -1; s->used = ++m->clock;
+    pthread_mutex_unlock(&g_pilot_mx);
+
+    load_expert_merged(m, layer, eid, s);
+
+    pthread_mutex_lock(&g_pilot_mx);
+    s->eid = eid;
+    s->pinned = m->is_pinned[layer * c->n_experts + eid];
+    s->used = ++m->clock;
+    m->is_queued[layer * c->n_experts + eid] = 0;
+    pthread_mutex_unlock(&g_pilot_mx);
+}
+
+static void *pilot_worker(void *arg) {
+    (void)arg;
+    while (1) {
+        unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
+        unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_ACQUIRE);
+        if (r == w) {
+            sleep_ms(1);
+            continue;
+        }
+        int layer = pilot_q[r & 4095].l;
+        int eid = pilot_q[r & 4095].e;
+        pilot_realload(pilot_m, layer, eid);
+        __atomic_store_n(&pilot_r, r + 1, __ATOMIC_RELEASE);
+    }
+    return NULL;
+}
+
+static void pilot_prefetch(Model *m, int lnext, const float *x, int S) {
+    if (lnext < 0 || lnext >= m->c.n_layers) return;
+    Cfg *c = &m->c; int D = c->hidden, E = c->n_experts;
+    ensure_pilot_worker_started(m);
+    float *logits = falloc((int64_t)S * E);
+    Layer *l = &m->L[lnext];
+
+    // PREDICTION IMPROVEMENT B: Apply RMSNorm to x using destination layer's post_ln
+    // This scales inputs to the distribution expected by l->gate.
+    float *nrm_x = falloc((int64_t)S * D);
+    for (int s = 0; s < S; s++) {
+        rmsnorm_row(nrm_x + (int64_t)s * D, x + (int64_t)s * D, l->post_ln, D, c->eps);
+    }
+
+    matmul(logits, nrm_x, l->gate, S, D, E);
+    free(nrm_x);
+
+    for (int s = 0; s < S; s++) {
+        float *pr = logits + (int64_t)s * E;
+
+        // PREDICTION IMPROVEMENT A: Apply routing momentum (EMA of gate logits)
+        float *blended = pr;
+        float *ema = m->momentum_logits + (int64_t)lnext * E;
+        if (m->pilot_smooth > 0.f) {
+            blended = falloc(E);
+            int is_zero = 1;
+            for (int e = 0; e < E; e++) { if (ema[e] != 0.f) { is_zero = 0; break; } }
+            if (is_zero) {
+                for (int e = 0; e < E; e++) {
+                    ema[e] = pr[e];
+                    blended[e] = pr[e];
+                }
+            } else {
+                for (int e = 0; e < E; e++) {
+                    blended[e] = (1.f - m->pilot_smooth) * pr[e] + m->pilot_smooth * ema[e];
+                    ema[e] = blended[e]; // update EMA
+                }
+            }
+        }
+
+        int cand = 0;
+        int idx[128];
+
+        float max_logit = -1e30f;
+        for (int e = 0; e < E; e++) { if (blended[e] > max_logit) max_logit = blended[e]; }
+        float *exps = falloc(E);
+        float sum_exps = 0.f;
+        for (int e = 0; e < E; e++) {
+            exps[e] = expf(blended[e] - max_logit);
+            sum_exps += exps[e];
+        }
+
+        float cum_sum = 0.f;
+        int min_cand = c->topk;
+        int max_cand = c->topk * g_wide;
+        if (max_cand < min_cand) max_cand = min_cand;
+        if (max_cand > 128) max_cand = 128; /* idx[] buffer bound */
+        if (max_cand > E) max_cand = E;
+
+        for (int kk = 0; kk < max_cand; kk++) {
+            int best = -1; float bv = -1.f;
+            for (int e = 0; e < E; e++) {
+                int taken = 0; for (int j = 0; j < kk; j++) if (idx[j] == e) { taken=1; break; }
+                if (!taken && exps[e] > bv) { bv = exps[e]; best = e; }
+            }
+            if (best < 0) break;
+            idx[kk] = best;
+            cum_sum += bv;
+            cand++;
+            if (cum_sum >= m->pilot_conf_limit * sum_exps && cand >= min_cand) {
+                break;
+            }
+        }
+        free(exps);
+
+        if (blended != pr) free(blended);
+
+        /* IMPROVEMENT 5: sort candidates by eid for sequential SSD read locality */
+        for (int a = 0; a < cand-1; a++)
+            for (int b = a+1; b < cand; b++)
+                if (idx[b] >= 0 && (idx[a] < 0 || idx[a] > idx[b])) { int t = idx[a]; idx[a] = idx[b]; idx[b] = t; }
+
+        for (int kk = 0; kk < cand; kk++) {
+            int eid = idx[kk];
+            if (eid < 0) continue;
+            int found = 0;
+            pthread_mutex_lock(&g_pilot_mx);
+            LCache *lc = &m->cache[lnext];
+            for (int z = 0; z < lc->n; z++) {
+                if (lc->slots[z].eid == eid) { found = 1; break; }
+            }
+            pthread_mutex_unlock(&g_pilot_mx);
+            if (!found) {
+                int gidx = lnext * E + eid;
+                pthread_mutex_lock(&g_pilot_mx);
+                int already_queued = m->is_queued[gidx];
+                if (!already_queued) {
+                    m->is_queued[gidx] = 1;
+                }
+                pthread_mutex_unlock(&g_pilot_mx);
+
+                if (!already_queued) {
+                    unsigned w2 = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
+                    unsigned r2 = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
+                    if (w2 - r2 < 4096) {
+                        pilot_q[w2 & 4095].l = lnext;
+                        pilot_q[w2 & 4095].e = eid;
+                        __atomic_store_n(&pilot_w, w2 + 1, __ATOMIC_RELEASE);
+                    } else {
+                        pthread_mutex_lock(&g_pilot_mx);
+                        m->is_queued[gidx] = 0;
+                        pthread_mutex_unlock(&g_pilot_mx);
+                    }
+                }
+            }
+        }
+    }
+    free(logits);
+}
+
+
 /* generazione greedy. prompt[np] -> riempie out[np+n_new] */
 static void generate(Model *m, const int *prompt, int np, int n_new, int *out) {
     Cfg *c = &m->c;
@@ -442,22 +903,32 @@ static int *read_int_array(jval *o, const char *key, int *n_out) {
 int main(int argc, char **argv) {
     const char *snap = getenv("SNAP");
     if (!snap) { fprintf(stderr, "set SNAP=<snapshot directory>\n"); return 1; }
-    int cap  = argc > 1 ? atoi(argv[1]) : 16;
-    int bits = argc > 2 ? atoi(argv[2]) : 8;
-    if (bits < 2 || bits > 8) {   /* expert storage is int8_t: bits>8 truncates in quantize_rows (#134). f32 mode is not implemented here — int8 is already token-exact vs the oracle. */
-        fprintf(stderr, "quant_bits must be 2..8 (got %d); OLMoE experts are int8-backed, no f32 mode\n", bits);
+    g_pilot = getenv("PILOT") ? atoi(getenv("PILOT")) : 0;
+    g_wide  = getenv("WIDE")  ? atoi(getenv("WIDE"))  : 1;
+    if (g_wide < 1) g_wide = 1;
+    if (g_wide > 4) g_wide = 4;
+    int hot_n  = getenv("HOT")   ? atoi(getenv("HOT"))   : 0;
+    int cap    = argc > 1 ? atoi(argv[1]) : 16;
+    int bits   = argc > 2 ? atoi(argv[2]) : 8;
+    if (bits < 2 || bits > 8) {
+        fprintf(stderr, "quant_bits must be 2..8 (got %d)\n", bits);
         return 1;
     }
     const char *refpath = argc > 3 ? argv[3] : "ref.json";
 
-    FILE *f = fopen(refpath, "rb"); if(!f){perror(refpath);return 1;}
+    float smooth = getenv("SMOOTH") ? (float)atof(getenv("SMOOTH")) : 0.3f;
+    float conf   = getenv("CONF_LIMIT") ? (float)atof(getenv("CONF_LIMIT")) : 0.92f;
+
+    printf("== Streaming C engine v2.2 | cache=%d/layer bits=%d pilot=%d wide=%d hot=%d smooth=%.2f conf=%.2f ==\n",
+           cap, bits, g_pilot, g_wide, hot_n, smooth, conf);
+
+    FILE *f = fopen(refpath, "rb"); if (!f) { perror(refpath); return 1; }
     fseek(f,0,SEEK_END); long n=ftell(f); fseek(f,0,SEEK_SET);
-    char *buf=malloc(n+1); if(fread(buf,1,n,f)!=(size_t)n){} buf[n]=0; fclose(f);
+    char *buf=malloc(n+1); if (fread(buf,1,n,f)!=(size_t)n) {} buf[n]=0; fclose(f);
     char *arena=NULL; jval *ref = json_parse(buf, &arena);
     int np, nfull; int *prompt = read_int_array(ref,"prompt_ids",&np); int *full = read_int_array(ref,"full_ids",&nfull);
     int n_new = nfull - np;
 
-    printf("== Streaming C engine, cache = %d experts/layer, experts @ %d-bit ==\n", cap, bits);
     Model m; model_init(&m, snap, cap, bits);
     printf("resident weights loaded in %.1fs | RSS after load: %.2f GB\n", m.dense_load_s, rss_gb());
 
@@ -487,6 +958,26 @@ int main(int argc, char **argv) {
     printf("\nPEAK RSS: %.2f GB\n", rss_gb());
     printf("Expert cache hit rate: %.1f%%  (hit=%llu miss=%llu)\n", tot?100.0*m.hits/tot:0.0,
            (unsigned long long)m.hits, (unsigned long long)m.miss);
+
+
+    // Persistent Hot Pinning: save dynamic pinning if newly created
+    if (m.hot_pinned) {
+        char pinpath[512];
+        snprintf(pinpath, sizeof(pinpath), "%s/hot_pinned.bin", snap);
+        FILE *pinf_chk = fopen(pinpath, "rb");
+        if (!pinf_chk) {
+            FILE *pinf_save = fopen(pinpath, "wb");
+            if (pinf_save) {
+                size_t expected_size = (size_t)m.c.n_layers * m.c.n_experts;
+                fwrite(m.is_pinned, 1, expected_size, pinf_save);
+                fclose(pinf_save);
+                printf("[HOT] Saved persistent pinning to %s\n", pinpath);
+            }
+        } else {
+            fclose(pinf_chk);
+        }
+    }
+
     printf("Speed: %.2f tok/s (%.1fs for %d tokens)\n", n_new/dt, dt, n_new);
     free(buf); free(arena);
     return 0;

c/ref_olmoe_real.json

@@ -0,0 +1,28 @@
+{
+  "prompt_ids": [
+    510,
+    5347,
+    273,
+    6181,
+    310
+  ],
+  "full_ids": [
+    510,
+    5347,
+    273,
+    6181,
+    310,
+    7785,
+    15,
+    187,
+    187,
+    510,
+    3565,
+    3448,
+    273,
+    6181,
+    310,
+    5112,
+    15
+  ]
+}

c/st.h

@@ -200,7 +200,19 @@ static void st_init(shards *S, const char *snap_dir) {
             if (a0 < 0 || b0 < a0 || data_start + b0 > fsz) {
                 fprintf(stderr, "%s: tensor '%s' data_offsets [%lld,%lld] out of file bounds (%lld)\n",
                         files[fi], name, (long long)a0, (long long)b0, (long long)fsz); exit(1); }
-            int64_t numel = 1; for (int k = 0; k < shp->len; k++) numel *= (int64_t)shp->kids[k]->num;
+            /* SEC: lo shape viene da un file non fidato (mirror). Senza il guard
+             * di overflow, uno shape tipo [65535,65535,65535,...] fa avvolgere
+             * numel a un valore piccolo/negativo che poi passerebbe il cross-check
+             * numel*esz==nbytes in st_read_f32, riaprendo l'OOB. */
+            int64_t numel = 1; int bad_shape = 0;
+            for (int k = 0; k < shp->len; k++) {
+                int64_t d = (int64_t)shp->kids[k]->num;
+                if (d < 0 || (d != 0 && numel > INT64_MAX / d)) { bad_shape = 1; break; }
+                numel *= d;
+            }
+            if (bad_shape) {
+                fprintf(stderr, "%s: tensor '%s' shape overflows int64 — refusing (hostile or corrupt file)\n",
+                        files[fi], name); exit(1); }
             if (S->n == S->cap) { S->cap *= 2; S->t = realloc(S->t, S->cap*sizeof(st_tensor)); }
             st_tensor *t = &S->t[S->n++];
             t->name = strdup(name); t->fd = fd; t->off = data_start + a0;
@@ -249,6 +261,15 @@ static void st_prefetch(shards *S, const char *name) {
 static int64_t st_read_f32(shards *S, const char *name, float *out, int drop) {
     st_tensor *t = st_find(S, name);
     if (!t) { fprintf(stderr, "missing tensor: %s\n", name); exit(1); }
+    /* SEC: numel viene dallo shape, nbytes dagli offset — due campi indipendenti
+     * del file. Se non concordano, la memcpy F32 (nbytes) o i loop BF16/F16
+     * (numel elementi da un raw di soli nbytes) sforano il buffer del chiamante,
+     * che e' dimensionato sul config, non sul file. Il chiamante che alloca su
+     * st_numel resta coerente; questo blocca l'ingresso ostile a monte. */
+    int esz = (t->dtype == 2) ? 4 : 2;
+    if (t->numel < 0 || t->numel > t->nbytes / esz || t->numel * (int64_t)esz != t->nbytes) {
+        fprintf(stderr, "%s: tensor '%s' shape/bytes mismatch (numel %lld, %lld bytes, dtype %d) — refusing (hostile or corrupt file)\n",
+                name, name, (long long)t->numel, (long long)t->nbytes, t->dtype); exit(1); }
     void *raw = malloc(t->nbytes);
     if (!raw) { fprintf(stderr, "malloc %lld bytes for tensor %s failed\n", (long long)t->nbytes, name); exit(1); }
     st_pread_full(t->fd, raw, t->nbytes, t->off, "pread data");

c/tools/convert_olmoe_merged.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+"""Convert OLMoE HuggingFace checkpoint to colibri merged int8 format.
+
+Consolidates gate_proj, up_proj, and down_proj into a single merged tensor per expert.
+This allows olmoe.c to load an expert in a single disk read call instead of 3.
+
+Usage:
+  python tools/convert_olmoe_merged.py --repo allenai/OLMoE-1B-7B-0125-Instruct --out ./olmoe_merged
+"""
+
+import argparse, json, os, sys, re
+from pathlib import Path
+
+# Windows: force UTF-8 output
+if sys.platform == "win32":
+    for s in (sys.stdout, sys.stderr):
+        try: s.reconfigure(encoding="utf-8")
+        except (AttributeError, OSError): pass
+
+try:
+    import torch
+    from safetensors.torch import load_file, save_file
+    import huggingface_hub
+except ImportError as exc:
+    sys.exit(f"Missing dependencies: {exc}. Install: pip install torch safetensors huggingface_hub")
+
+EXPERT_KEY_RE = r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight"
+
+def quantize_row(w: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Row-wise int8 quantization. Returns (int8_weights, float32_scales)."""
+    w_f32 = w.float()
+    row_max = w_f32.abs().amax(dim=1, keepdim=True).clamp(min=1e-12)
+    scales = row_max / 127.0
+    q = (w_f32 / scales).round().clamp(-128, 127).to(torch.int8)
+    return q, scales.squeeze(1)
+
+def main():
+    ap = argparse.ArgumentParser(description="Convert OLMoE HF checkpoint -> colibri merged int8")
+    src = ap.add_mutually_exclusive_group(required=True)
+    src.add_argument("--repo", help="HuggingFace repo ID")
+    src.add_argument("--model", help="Local HF checkpoint directory")
+    ap.add_argument("--out", required=True, help="Output directory for merged model")
+    args = ap.parse_args()
+
+    if args.repo:
+        from huggingface_hub import snapshot_download
+        from huggingface_hub.errors import LocalEntryNotFoundError
+        print(f"Downloading/Resolving {args.repo}...")
+        try:
+            src_dir = snapshot_download(args.repo, local_files_only=True, max_workers=4)
+        except LocalEntryNotFoundError:
+            src_dir = None
+        if src_dir is None or not any(Path(src_dir).glob("*.safetensors")):
+            print("Downloading safetensors...")
+            src_dir = snapshot_download(args.repo, max_workers=4)
+    else:
+        src_dir = args.model
+
+    src = Path(src_dir)
+    if not src.is_dir():
+        sys.exit(f"Model directory not found: {src}")
+    if not (src / "config.json").is_file():
+        sys.exit(f"config.json missing in {src}")
+
+    out = Path(args.out)
+    out.mkdir(parents=True, exist_ok=True)
+
+    # Copy config.json
+    import shutil
+    shutil.copy2(src / "config.json", out / "config.json")
+    print(f"config.json -> {out}")
+
+    # Process safetensors
+    shards = sorted(src.glob("*.safetensors"))
+    if not shards:
+        sys.exit(f"No safetensors found in {src}")
+
+    print("Loading all shards to build complete state dict...")
+    state_dict = {}
+    for si, shard in enumerate(shards, 1):
+        print(f"Loading shard {si}/{len(shards)}: {shard.name}...")
+        tensors = load_file(str(shard))
+        state_dict.update(tensors)
+
+    # Gather experts
+    experts = {}
+    for name in list(state_dict.keys()):
+        m = re.match(EXPERT_KEY_RE, name)
+        if m:
+            layer_idx, expert_idx, proj = m.groups()
+            layer_idx = int(layer_idx)
+            expert_idx = int(expert_idx)
+            key = (layer_idx, expert_idx)
+            if key not in experts:
+                experts[key] = {}
+            experts[key][proj] = state_dict.pop(name)
+
+    print(f"Found {len(experts)} experts to merge.")
+
+    # Process and merge experts
+    out_tensors = {}
+    total_expert_f32 = 0
+    total_expert_q = 0
+
+    for (layer, expert), projs in sorted(experts.items()):
+        if not ("gate_proj" in projs and "up_proj" in projs and "down_proj" in projs):
+            sys.exit(f"Missing projection for layer {layer} expert {expert}!")
+
+        gate = projs["gate_proj"]
+        up = projs["up_proj"]
+        down = projs["down_proj"]
+
+        total_expert_f32 += (gate.numel() + up.numel() + down.numel()) * gate.element_size()
+
+        # Quantize each projection separately
+        q_gate, s_gate = quantize_row(gate)
+        q_up, s_up = quantize_row(up)
+        q_down, s_down = quantize_row(down)
+
+        # Merge weights and scales contiguously
+        merged_q = torch.cat([q_gate.flatten(), q_up.flatten(), q_down.flatten()])
+        merged_scales = torch.cat([s_gate, s_up, s_down])
+
+        total_expert_q += merged_q.numel() * 1 + merged_scales.numel() * 4
+
+        # Save to output
+        out_tensors[f"model.layers.{layer}.mlp.experts.{expert}.merged_weight"] = merged_q
+        out_tensors[f"model.layers.{layer}.mlp.experts.{expert}.qs"] = merged_scales
+
+    # Copy remaining dense tensors
+    print(f"Adding remaining {len(state_dict)} dense tensors...")
+    out_tensors.update(state_dict)
+
+    # Save to a single output safetensors file for simpler loading
+    out_file = out / "model.safetensors"
+    print(f"Saving merged safetensors model to {out_file}...")
+    save_file(out_tensors, str(out_file))
+
+    ratio = total_expert_q / max(total_expert_f32, 1) * 100
+    print(f"\nDone. {len(experts)} experts successfully merged and saved.")
+    print(f"Expert storage: {total_expert_f32/1e9:.1f} GB -> {total_expert_q/1e9:.1f} GB ({ratio:.0f}%)")
+    print(f"Model ready at: {out}")
+
+if __name__ == "__main__":
+    main()

c/tools/make_olmoe_real_oracle.py

@@ -0,0 +1,70 @@
+"""Generate reference token IDs for the real OLMoE-1B-7B model.
+
+Uses the HF model loaded from the local cache to produce a small
+reference output for olmoe.exe validation. Saves to ref_olmoe_real.json.
+
+Usage: python tools/make_olmoe_real_oracle.py
+"""
+import json
+import sys
+from pathlib import Path
+
+if sys.platform == "win32":
+    for s in (sys.stdout, sys.stderr):
+        try:
+            s.reconfigure(encoding="utf-8")
+        except (AttributeError, OSError):
+            pass
+
+try:
+    import torch
+    from transformers import AutoTokenizer, OlmoeForCausalLM
+except ImportError as exc:
+    sys.exit(f"Missing deps: {exc}. Run: pip install torch transformers")
+
+MODEL_ID = "allenai/OLMoE-1B-7B-0125-Instruct"
+
+OUT_JSON = Path(__file__).resolve().parent.parent / "ref_olmoe_real.json"
+
+PROMPT = "The capital of France is"
+MAX_NEW_TOKENS = 12
+
+print(f"Loading tokenizer from {MODEL_ID} ...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+print("Encoding prompt ...")
+enc = tokenizer(PROMPT, return_tensors="pt")
+prompt_ids = enc["input_ids"][0].tolist()
+print(f"  Prompt IDs ({len(prompt_ids)}): {prompt_ids}")
+
+print(f"Loading OLMoE model from {MODEL_ID} ...")
+print("  (this will use ~14 GB RAM — please be patient)")
+model = OlmoeForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    device_map="cpu",
+    low_cpu_mem_usage=True,
+)
+model.eval()
+print("  Model loaded!")
+
+print(f"Generating {MAX_NEW_TOKENS} tokens ...")
+with torch.no_grad():
+    out = model.generate(
+        enc["input_ids"],
+        max_new_tokens=MAX_NEW_TOKENS,
+        do_sample=False,
+        use_cache=True,
+    )
+
+full_ids = out[0].tolist()
+gen_ids = full_ids[len(prompt_ids):]
+
+print(f"Prompt IDs  : {prompt_ids}")
+print(f"Full IDs    : {full_ids}")
+print(f"Generated   : {gen_ids}")
+print(f"Text        : {tokenizer.decode(gen_ids, skip_special_tokens=True)!r}")
+
+payload = {"prompt_ids": prompt_ids, "full_ids": full_ids}
+OUT_JSON.write_text(json.dumps(payload, indent=2))
+print(f"\nSaved reference to {OUT_JSON}")

c/tools/test_olmoe_real.py

@@ -0,0 +1,91 @@
+"""Bootstrap ref_olmoe_real.json by running olmoe.exe once and capturing output.
+
+Step 1: Creates a temp ref with only prompt_ids (no full_ids).
+Step 2: Runs olmoe.exe, parses the generated IDs from stdout.
+Step 3: Saves {prompt_ids, full_ids} as ref_olmoe_real.json.
+Step 4: Runs olmoe.exe again against the saved ref to verify determinism.
+
+No RAM loading of the full model -- the engine streams from SSD as designed.
+"""
+import json
+import os
+import re
+import subprocess
+import sys
+from pathlib import Path
+
+if sys.platform == "win32":
+    for s in (sys.stdout, sys.stderr):
+        try:
+            s.reconfigure(encoding="utf-8")
+        except (AttributeError, OSError):
+            pass
+
+HERE = Path(__file__).resolve().parent.parent
+ext = ".exe" if sys.platform == "win32" else ""
+ENGINE = HERE / f"olmoe{ext}"
+SNAP = os.getenv("SNAP", str(HERE.parent / "olmoe_merged"))
+REF_OUT = HERE / "ref_olmoe_real.json"
+BOOTSTRAP_REF = HERE / "ref_olmoe_bootstrap.json"
+
+PROMPT_IDS = [510, 5347, 273, 6181, 310]  # "The capital of France is"
+MAX_NEW = 12
+CACHE_SIZE = 32   # experts cached per layer
+QUANT_BITS = 8    # engine supports 2-8; 8 = int8 (lossless vs our quant)
+
+# ── Step 1: Write bootstrap ref with dummy full_ids = prompt_ids ──────────
+# olmoe.exe needs full_ids to know how many tokens to generate (nfull - np).
+# We extend with MAX_NEW zeros so the engine generates MAX_NEW tokens.
+bootstrap = {
+    "prompt_ids": PROMPT_IDS,
+    "full_ids": PROMPT_IDS + [0] * MAX_NEW,
+}
+BOOTSTRAP_REF.write_text(json.dumps(bootstrap))
+print(f"Bootstrap ref written to {BOOTSTRAP_REF}")
+
+env = {**os.environ, "SNAP": str(SNAP)}
+
+# ── Step 2: Run engine once to capture generated IDs ─────────────────────
+print(f"\n{'='*60}")
+print(f"Run 1/2 — capturing engine output (cache={CACHE_SIZE}, bits={QUANT_BITS}) ...")
+print(f"{'='*60}")
+cmd = [str(ENGINE), str(CACHE_SIZE), str(QUANT_BITS), str(BOOTSTRAP_REF)]
+r1 = subprocess.run(cmd, env=env, capture_output=True, text=True, cwd=str(HERE))
+print(r1.stdout)
+if r1.returncode != 0:
+    print("STDERR:", r1.stderr, file=sys.stderr)
+    sys.exit(r1.returncode)
+
+# Parse "C engine : <id> <id> ..." line
+m = re.search(r"C engine\s*:\s*([\d ]+)", r1.stdout)
+if not m:
+    sys.exit("Could not parse 'C engine :' line from output")
+gen_ids = [int(x) for x in m.group(1).split()]
+print(f"Captured generated IDs: {gen_ids}")
+
+full_ids = PROMPT_IDS + gen_ids
+real_ref = {"prompt_ids": PROMPT_IDS, "full_ids": full_ids}
+REF_OUT.write_text(json.dumps(real_ref, indent=2))
+print(f"\nReal reference saved to {REF_OUT}")
+
+# ── Step 3: Run engine again against real ref — verify determinism ────────
+print(f"\n{'='*60}")
+print("Run 2/2 — verifying determinism ...")
+print(f"{'='*60}")
+cmd2 = [str(ENGINE), str(CACHE_SIZE), str(QUANT_BITS), str(REF_OUT)]
+r2 = subprocess.run(cmd2, env=env, capture_output=True, text=True, cwd=str(HERE))
+print(r2.stdout)
+if r2.returncode != 0:
+    print("STDERR:", r2.stderr, file=sys.stderr)
+    sys.exit(r2.returncode)
+
+if "Matching tokens: 12/12" in r2.stdout or f"Matching tokens: {MAX_NEW}/{MAX_NEW}" in r2.stdout:
+    print("✓ Engine is DETERMINISTIC — same output on both runs!")
+else:
+    m2 = re.search(r"Matching tokens: (\d+)/(\d+)", r2.stdout)
+    if m2:
+        print(f"⚠ Partial match: {m2.group(0)} — engine may be non-deterministic")
+    else:
+        print("⚠ Could not find matching tokens line")
+
+BOOTSTRAP_REF.unlink(missing_ok=True)

colibri/__init__.py

@@ -0,0 +1,5 @@
+"""colibrì — tiny engine, immense model."""
+
+from colibri._version import __version__
+
+__all__ = ["__version__"]

colibri/_version.py

@@ -0,0 +1,23 @@
+"""Version accessor for the pip package.
+
+The single source of truth is c/version.py (#394): coli --version and the
+GitHub Release workflow read it, so the pip metadata must read the SAME file
+instead of carrying a second literal that would drift on the first bump.
+
+From a checkout (the supported install: `pip install -e .`) the file is read
+directly. From an installed wheel c/ is not on disk, so fall back to the
+package metadata that setuptools baked at build time from that same file.
+"""
+from pathlib import Path
+
+try:
+    _ns = {}
+    exec((Path(__file__).resolve().parent.parent / "c" / "version.py").read_text(), _ns)
+    __version__ = _ns["__version__"]
+except OSError:
+    from importlib.metadata import PackageNotFoundError, version
+
+    try:
+        __version__ = version("colibri-engine")
+    except PackageNotFoundError:
+        __version__ = "0.0.0+unknown"

colibri/cli.py

@@ -0,0 +1,30 @@
+"""Entry point for `coli` when installed via pip.
+
+Delegates to the original c/coli script which handles all subcommands.
+This wrapper exists so `pip install colibri-engine` creates a `coli` console
+script that works without the user having to add c/ to PATH manually.
+"""
+
+import os
+import sys
+import runpy
+
+
+def main():
+    here = os.path.dirname(os.path.abspath(__file__))
+    engine_dir = os.path.join(os.path.dirname(here), "c")
+    coli_script = os.path.join(engine_dir, "coli")
+
+    if not os.path.exists(coli_script):
+        sys.exit(
+            "colibri engine directory not found.\n"
+            "Install from source: git clone + pip install -e ."
+        )
+
+    sys.path.insert(0, engine_dir)
+    sys.argv[0] = coli_script
+    runpy.run_path(coli_script, run_name="__main__")
+
+
+if __name__ == "__main__":
+    main()

docs/quickstart.md

@@ -0,0 +1,164 @@
+# Quick Start — from zero to a running model
+
+A step-by-step guide for first-time users on **Linux**, **Windows**, and **macOS**.
+No prior experience with C, CUDA, or model conversion is assumed. If you get
+stuck, `./coli doctor` (below) tells you exactly what's missing.
+
+> **What you're setting up:** colibrì runs a very large Mixture-of-Experts model
+> (e.g. GLM-5.2, 744B parameters) on a normal machine by streaming the model's
+> experts from disk instead of needing them all in RAM. The engine is a single
+> C program; Python is only used once, to prepare the model files.
+
+---
+
+## 0. What you need first (prerequisites)
+
+| | Minimum | Recommended |
+|---|---|---|
+| **RAM** | ~16 GB | 24 GB+ |
+| **Free disk** | ~380 GB for the int4 model | a fast NVMe SSD (streaming speed = your token speed) |
+| **OS** | Linux, Windows 10/11, or macOS | any |
+| **Tools** | a C compiler + `make` + `git` + `python3` | — |
+
+You do **not** need a GPU. A GPU only helps if you have one; the engine runs
+CPU-only by default.
+
+---
+
+## 1. Install the build tools
+
+### Linux (Ubuntu / Debian)
+
+```bash
+sudo apt update
+sudo apt install -y build-essential git python3
+```
+
+`build-essential` gives you `gcc`, `make`, and OpenMP (libgomp) — everything the
+engine needs.
+
+### Windows
+
+You have two options.
+
+**Option A — download a prebuilt binary (no compiler needed).**
+Grab the latest `colibri-<version>-windows-x86_64.zip` from the
+[Releases page](https://github.com/JustVugg/colibri/releases), unzip it, and
+skip to [step 3](#3-get-the-model). Python 3 (from
+[python.org](https://www.python.org/downloads/)) is still needed if you want to
+convert a model yourself.
+
+**Option B — build from source with MSYS2.**
+Install [MSYS2](https://www.msys2.org/), open the **UCRT64** shell, and run:
+
+```bash
+pacman -S --needed mingw-w64-ucrt-x86_64-gcc make git python
+```
+
+### macOS
+
+```bash
+xcode-select --install          # C compiler (clang)
+brew install libomp git python  # OpenMP for multithreading
+```
+
+---
+
+## 2. Get the code and build the engine
+
+```bash
+git clone https://github.com/JustVugg/colibri.git
+cd colibri/c
+./setup.sh
+```
+
+`setup.sh` checks your compiler and OpenMP, builds the engine, and runs a tiny
+self-test. When it prints:
+
+```
+engine self-test: 32/32  (expected 32/32)
+```
+
+the engine is working correctly. (On Windows Option A you already have the
+binary — you can skip this step.)
+
+---
+
+## 3. Get the model
+
+You have two paths.
+
+### Easiest — download a ready-made int4 container
+
+A pre-converted **GLM-5.2 int4** model is on Hugging Face. **Use the version
+with the int8 MTP heads** (the plain int4 heads disable speculative decoding —
+see [#8](https://github.com/JustVugg/colibri/issues/8)):
+
+**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp**
+
+Download it into a folder on a fast disk, e.g. `/nvme/glm52_i4` (Linux/macOS) or
+`D:\glm52_i4` (Windows). It is about **372 GB**, so make sure you have the space.
+
+### Or convert it yourself from the FP8 source
+
+One resumable command downloads and converts the model shard by shard, so it
+never needs the full ~756 GB on disk at once:
+
+```bash
+./coli convert --model /nvme/glm52_i4
+```
+
+This step uses Python and runs only once. Safe to interrupt and re-run — it
+resumes where it left off.
+
+---
+
+## 4. Run it
+
+Point `COLI_MODEL` at the folder from step 3 and start chatting:
+
+```bash
+# Linux / macOS
+COLI_MODEL=/nvme/glm52_i4 ./coli chat
+
+# Windows (UCRT64 shell)
+COLI_MODEL=/d/glm52_i4 ./coli chat
+```
+
+Useful first commands:
+
+```bash
+COLI_MODEL=/nvme/glm52_i4 ./coli doctor   # read-only check: is everything ready?
+COLI_MODEL=/nvme/glm52_i4 ./coli plan     # shows where the model will live (RAM/disk/GPU)
+COLI_MODEL=/nvme/glm52_i4 ./coli chat --topp 0.85   # faster: reads less from disk, same quality
+```
+
+> **Tip:** `--topp 0.85` is worth adding on a disk-bound machine — it reads
+> fewer expert bytes per token with no quality loss, which directly means more
+> tokens per second.
+
+---
+
+## 5. What to expect
+
+- **First launch loads the resident weights** (~10 GB) — this takes a moment.
+- **Speed depends on your disk.** The experts stream from storage, so a fast
+  NVMe SSD is the single biggest factor in tokens/second. On a slow or shared
+  disk, generation can be well under 1 token/second — that's expected, and it's
+  the honest cost of running a 744B model on a small machine.
+- **It's still the full model.** Placement only changes speed, never the model's
+  answers or precision.
+
+If something doesn't work, run `./coli doctor` — it reports exactly what's
+missing (compiler, model files, permissions) and how to fix it.
+
+---
+
+## Where to go next
+
+| Topic | Doc |
+|---|---|
+| Windows native build (and CUDA DLL) | [docs/windows.md](windows.md) |
+| Tuning: cache, prefetch, speculation | [docs/tuning.md](tuning.md) |
+| OpenAI-compatible API + web dashboard | [docs/api.md](api.md) |
+| Every environment variable | [docs/ENVIRONMENT.md](ENVIRONMENT.md) |

pyproject.toml

@@ -0,0 +1,53 @@
+[build-system]
+requires = ["setuptools>=68.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "colibri-engine"
+dynamic = ["version"]
+description = "Tiny engine, immense model — run GLM-5.2 (744B MoE) locally"
+readme = "README.md"
+license = "Apache-2.0"
+requires-python = ">=3.10"
+authors = [
+    {name = "JustVugg"},
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "Operating System :: POSIX :: Linux",
+    "Operating System :: MacOS",
+    "Operating System :: Microsoft :: Windows",
+    "Programming Language :: Python :: 3",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+
+[project.optional-dependencies]
+convert = [
+    "numpy",
+    "huggingface_hub",
+]
+oracle = [
+    "torch>=2.0",
+    "transformers>=4.40",
+    "safetensors",
+]
+bench = [
+    "tokenizers",
+    "datasets",
+]
+
+[project.scripts]
+coli = "colibri.cli:main"
+
+[project.urls]
+Homepage = "https://github.com/JustVugg/colibri"
+Issues = "https://github.com/JustVugg/colibri/issues"
+
+[tool.setuptools.dynamic]
+version = {attr = "colibri._version.__version__"}
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["colibri*"]