Instructions to use TessaCoil/K3-Stuff 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 TessaCoil/K3-Stuff 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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: llama cli -hf TessaCoil/K3-Stuff:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: llama cli -hf TessaCoil/K3-Stuff:Q8_0
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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf TessaCoil/K3-Stuff:Q8_0
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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf TessaCoil/K3-Stuff:Q8_0
Use Docker
docker model run hf.co/TessaCoil/K3-Stuff:Q8_0
- LM Studio
- Jan
- Ollama
How to use TessaCoil/K3-Stuff with Ollama:
ollama run hf.co/TessaCoil/K3-Stuff:Q8_0
- Unsloth Desktop
- Pi
How to use TessaCoil/K3-Stuff with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "TessaCoil/K3-Stuff:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use TessaCoil/K3-Stuff with Docker Model Runner:
docker model run hf.co/TessaCoil/K3-Stuff:Q8_0
- Lemonade
How to use TessaCoil/K3-Stuff with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TessaCoil/K3-Stuff:Q8_0
Run and chat with the model
lemonade run user.K3-Stuff-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use TessaCoil/K3-Stuff with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default TessaCoil/K3-Stuff:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TessaCoil/K3-Stuff with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "TessaCoil/K3-Stuff:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 11,893 Bytes
ddf8c5b | 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 | // requant_trunk.c — Surgical GGUF rewriter for Kimi-K3 UD-Q4_K_XL.
//
// Requantizes ONLY the Q8_0 "trunk" tensors (attention, shared experts, output,
// token embedding) from Q8_0 -> Q4_K. Byte-copies everything else unchanged:
// - MXFP4 routed-expert tensors (ffn_*_exps) : QAT-native, must NOT requant
// - F32/BF16 norm & bias tensors : tiny, keep full precision
//
// Why not llama-quantize? In --allow-requantize mode it forces EVERY non-overridden
// tensor to the positional type, which would dequant->requant the MXFP4 experts and
// destroy their QAT calibration. We must byte-preserve the experts.
//
// Processes one shard at a time (split-in = split-out, like --keep-split).
//
// Build (on box):
// gcc -O2 -o requant_trunk requant_trunk.c \
// -I/root/llama.cpp/ggml/include -I/root/llama.cpp/ggml/src \
// /root/llama.cpp/build/ggml/src/libggml-base.a \
// /root/llama.cpp/build/ggml/src/libggml-cpu.a \
// /root/llama.cpp/build/ggml/src/libggml.a -lm -lpthread
//
// Usage: requant_trunk <in_shard.gguf> <out_shard.gguf>
//
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.h>
#include <stdbool.h>
#include "ggml.h"
#include "ggml-quants.h"
#define GGUF_MAGIC 0x46554747 // "GGUF"
#define ALIGNMENT 32
// ---- little-endian read helpers ----
static uint32_t rd_u32(FILE *f){ uint32_t v; if(fread(&v,4,1,f)!=1){fprintf(stderr,"EOF u32\n");exit(1);} return v; }
static uint64_t rd_u64(FILE *f){ uint64_t v; if(fread(&v,8,1,f)!=1){fprintf(stderr,"EOF u64\n");exit(1);} return v; }
// ---- write helpers (dynamic buffer for header) ----
typedef struct { uint8_t *data; size_t len, cap; } Buf;
static void buf_put(Buf *b, const void *p, size_t n){
if (b->len + n > b->cap){ b->cap = (b->len + n)*2 + 1024; b->data = realloc(b->data, b->cap); if(!b->data){fprintf(stderr,"oom\n");exit(1);} }
memcpy(b->data + b->len, p, n); b->len += n;
}
static void buf_u32(Buf *b, uint32_t v){ buf_put(b, &v, 4); }
static void buf_u64(Buf *b, uint64_t v){ buf_put(b, &v, 8); }
// skip a KV value of given vtype in input file
static void skip_kv_value(FILE *f, uint32_t vtype){
switch(vtype){
case 0: case 1: case 7: fseek(f,1,SEEK_CUR); break;
case 2: case 3: fseek(f,2,SEEK_CUR); break;
case 4: case 5: case 6: fseek(f,4,SEEK_CUR); break;
case 10: case 11: case 12: fseek(f,8,SEEK_CUR); break;
case 8: { uint64_t l=rd_u64(f); fseek(f,(long)l,SEEK_CUR); } break;
case 9: {
uint32_t at=rd_u32(f); uint64_t al=rd_u64(f);
if (at==8){ for(uint64_t i=0;i<al;i++){ uint64_t sl=rd_u64(f); fseek(f,(long)sl,SEEK_CUR);} }
else {
int sz; switch(at){case 0:case 1:case 7:sz=1;break;case 2:case 3:sz=2;break;case 4:case 5:case 6:sz=4;break;default:sz=8;}
fseek(f,(long)(al*sz),SEEK_CUR);
}
} break;
default: fprintf(stderr,"bad vtype %u\n",vtype); exit(1);
}
}
// copy a KV value verbatim from input file into buffer
static void copy_kv_value(FILE *f, Buf *out, uint32_t vtype){
buf_u32(out, vtype);
switch(vtype){
case 0: case 1: case 7: { uint8_t b[1]; if(fread(b,1,1,f)!=1){exit(1);} buf_put(out,b,1);} break;
case 2: case 3: { uint8_t b[2]; if(fread(b,2,1,f)!=1){exit(1);} buf_put(out,b,2);} break;
case 4: case 5: case 6: { uint8_t b[4]; if(fread(b,4,1,f)!=1){exit(1);} buf_put(out,b,4);} break;
case 10: case 11: case 12: { uint8_t b[8]; if(fread(b,8,1,f)!=1){exit(1);} buf_put(out,b,8);} break;
case 8: { uint64_t l=rd_u64(f); buf_u64(out,l); uint8_t *tmp=malloc(l); if(fread(tmp,l,1,f)!=1){exit(1);} buf_put(out,tmp,l); free(tmp);} break;
case 9: {
uint32_t at=rd_u32(f); uint64_t al=rd_u64(f);
buf_u32(out,at); buf_u64(out,al);
if (at==8){ for(uint64_t i=0;i<al;i++){ uint64_t sl=rd_u64(f); buf_u64(out,sl); uint8_t *tmp=malloc(sl); if(fread(tmp,sl,1,f)!=1){exit(1);} buf_put(out,tmp,sl); free(tmp);} }
else { int sz; switch(at){case 0:case 1:case 7:sz=1;break;case 2:case 3:sz=2;break;case 4:case 5:case 6:sz=4;break;default:sz=8;}
uint64_t nb=al*sz; uint8_t *tmp=malloc(nb); if(fread(tmp,nb,1,f)!=1){exit(1);} buf_put(out,tmp,nb); free(tmp); }
} break;
default: fprintf(stderr,"bad vtype %u\n",vtype); exit(1);
}
}
// decide whether a tensor name is a routed expert (MXFP4, byte-copy)
static bool is_routed_expert(const char *name){
return strstr(name, "_exps.") != NULL; // ffn_gate_exps / ffn_up_exps / ffn_down_exps
}
int main(int argc, char **argv){
if (argc < 3){ fprintf(stderr,"usage: %s <in> <out>\n", argv[0]); return 1; }
const char *fin_name = argv[1], *fout_name = argv[2];
FILE *fin = fopen(fin_name, "rb"); if(!fin){ perror("open in"); return 1; }
FILE *fout = fopen(fout_name, "wb"); if(!fout){ perror("open out"); return 1; }
// ---- header ----
uint32_t magic = rd_u32(fin);
if (magic != GGUF_MAGIC){ fprintf(stderr,"not GGUF\n"); return 1; }
uint32_t version = rd_u32(fin);
uint64_t n_tensors = rd_u64(fin);
uint64_t n_kv = rd_u64(fin);
fprintf(stderr,"[requant] %s: ver=%u tensors=%llu kv=%llu\n", fin_name, version,
(unsigned long long)n_tensors, (unsigned long long)n_kv);
Buf hdr = {0};
buf_u32(&hdr, magic); buf_u32(&hdr, version);
buf_u64(&hdr, n_tensors); buf_u64(&hdr, n_kv);
// ---- copy KV verbatim ----
for (uint64_t i=0;i<n_kv;i++){
uint64_t klen = rd_u64(fin);
uint8_t *key = malloc(klen); if(fread(key,klen,1,fin)!=1){exit(1);}
uint32_t vtype = rd_u32(fin);
buf_u64(&hdr, klen); buf_put(&hdr, key, klen);
copy_kv_value(fin, &hdr, vtype);
free(key);
}
// ---- tensor infos: read all, decide new type, record ----
typedef struct {
char *name; uint32_t n_dims; uint64_t dims[8]; uint32_t orig_type; uint32_t new_type;
uint64_t orig_offset; // offset within data section
uint64_t orig_size; // bytes in source
uint64_t new_size; // bytes in output
bool requant; // true => q8_0 -> q4_K
} TInfo;
TInfo *tis = calloc(n_tensors, sizeof(TInfo));
uint64_t n_requant=0, n_copy=0, bytes_in=0, bytes_out=0;
for (uint64_t i=0;i<n_tensors;i++){
TInfo *ti = &tis[i];
uint64_t nl = rd_u64(fin);
ti->name = malloc(nl+1); if(fread(ti->name,nl,1,fin)!=1){exit(1);} ti->name[nl]=0;
ti->n_dims = rd_u32(fin);
uint64_t nelem = 1;
for (uint32_t d=0; d<ti->n_dims; d++){ ti->dims[d]=rd_u64(fin); nelem *= ti->dims[d]; }
ti->orig_type = rd_u32(fin);
ti->orig_offset = rd_u64(fin);
enum ggml_type ot = (enum ggml_type)ti->orig_type;
// total tensor bytes = row_size(type, ne[0]) * ne[1]*ne[2]*ne[3]
int64_t nrow_mult = 1;
for (uint32_t d=1; d<ti->n_dims; d++) nrow_mult *= (int64_t)ti->dims[d];
ti->orig_size = ggml_row_size(ot, (int64_t)ti->dims[0]) * nrow_mult;
// decision: requant Q8_0 non-expert tensors to Q4_K; copy everything else
if (ot == GGML_TYPE_Q8_0 && !is_routed_expert(ti->name)){
ti->requant = true;
ti->new_type = GGML_TYPE_Q4_K;
ti->new_size = ggml_row_size(GGML_TYPE_Q4_K, (int64_t)ti->dims[0]) * nrow_mult;
n_requant++;
} else {
ti->requant = false;
ti->new_type = ti->orig_type;
ti->new_size = ti->orig_size;
n_copy++;
}
// sanity: Q4_K requires the row dim divisible by QK_K(256). If not, copy instead.
if (ti->requant && (ti->dims[0] % 256) != 0){
fprintf(stderr," [warn] %s dims[0]=%llu not mult of 256, copying instead\n", ti->name,(unsigned long long)ti->dims[0]);
ti->requant=false; ti->new_type=ti->orig_type; ti->new_size=ti->orig_size; n_requant--; n_copy++;
}
bytes_in += ti->orig_size; bytes_out += ti->new_size;
}
fprintf(stderr,"[requant] tensors: %llu requant(q8_0->q4_K), %llu copy | data %.1f GB -> %.1f GB\n",
(unsigned long long)n_requant,(unsigned long long)n_copy, bytes_in/1e9, bytes_out/1e9);
// ---- write tensor infos with new types & recomputed offsets ----
// first compute new data offsets (sequential, aligned per-tensor to ALIGNMENT within data section)
uint64_t *new_off = calloc(n_tensors, sizeof(uint64_t));
uint64_t cur = 0;
for (uint64_t i=0;i<n_tensors;i++){
// each tensor's data starts aligned relative to data section start
new_off[i] = cur;
uint64_t sz = tis[i].new_size;
// pad to ALIGNMENT after each tensor
cur += (sz + ALIGNMENT - 1)/ALIGNMENT*ALIGNMENT;
}
Buf tinfos = {0};
for (uint64_t i=0;i<n_tensors;i++){
TInfo *ti=&tis[i];
uint64_t nl=strlen(ti->name);
buf_u64(&tinfos, nl); buf_put(&tinfos, ti->name, nl);
buf_u32(&tinfos, ti->n_dims);
for (uint32_t d=0; d<ti->n_dims; d++) buf_u64(&tinfos, ti->dims[d]);
buf_u32(&tinfos, ti->new_type);
buf_u64(&tinfos, new_off[i]);
}
// ---- emit header + tinfos, pad to data start ----
uint64_t head_len = hdr.len + tinfos.len;
uint64_t data_start = (head_len + ALIGNMENT - 1)/ALIGNMENT*ALIGNMENT;
fwrite(hdr.data, hdr.len, 1, fout);
fwrite(tinfos.data, tinfos.len, 1, fout);
for (uint64_t p=head_len; p<data_start; p++) fputc(0, fout);
// ---- source data section start ----
// we must know where source tensor data begins to seek by orig_offset.
// Recompute: after reading all tensor infos from fin, current position = end of tinfos.
uint64_t src_tinfos_end = (uint64_t)ftell(fin);
uint64_t src_data_start = (src_tinfos_end + ALIGNMENT - 1)/ALIGNMENT*ALIGNMENT;
// ---- process tensors: copy or requant ----
float *fbuf = NULL; void *qbuf = NULL; size_t fbuf_n=0, qbuf_n=0;
uint64_t done=0;
for (uint64_t i=0;i<n_tensors;i++){
TInfo *ti=&tis[i];
// read source tensor data
fseek(fin, (long)(src_data_start + ti->orig_offset), SEEK_SET);
if (!ti->requant){
// byte-copy
uint8_t *tmp = malloc(ti->orig_size);
if (fread(tmp, ti->orig_size, 1, fin)!=1){ fprintf(stderr,"read tensor %s fail\n",ti->name); return 1; }
// write at new offset
uint64_t pos = data_start + new_off[i];
fseek(fout, (long)pos, SEEK_SET);
fwrite(tmp, ti->orig_size, 1, fout);
free(tmp);
} else {
int64_t nelem = 1; for (uint32_t d=0; d<ti->n_dims; d++) nelem *= (int64_t)ti->dims[d];
if ((size_t)nelem > fbuf_n){ fbuf = realloc(fbuf, nelem*sizeof(float)); fbuf_n=nelem; }
if (ti->orig_size > qbuf_n){ qbuf = realloc(qbuf, ti->orig_size); qbuf_n=ti->orig_size; }
// read q8_0
if (fread(qbuf, ti->orig_size, 1, fin)!=1){ fprintf(stderr,"read q8 %s fail\n",ti->name); return 1; }
// dequant q8_0 -> f32
dequantize_row_q8_0((const block_q8_0*)qbuf, fbuf, nelem);
// requant f32 -> q4_K (into a second region; reuse qbuf after? need separate)
void *out = malloc(ti->new_size);
quantize_row_q4_K_ref(fbuf, (block_q4_K*)out, nelem);
uint64_t pos = data_start + new_off[i];
fseek(fout, (long)pos, SEEK_SET);
fwrite(out, ti->new_size, 1, fout);
free(out);
}
done++;
if (done % 50 == 0 || done==n_tensors){
fprintf(stderr,"\r[requant] %llu/%llu tensors", (unsigned long long)done,(unsigned long long)n_tensors);
fflush(stderr);
}
}
fprintf(stderr,"\n[requant] wrote %s\n", fout_name);
fclose(fin); fclose(fout);
return 0;
}
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