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: 12,387 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 | #!/usr/bin/env python3
"""Fix the DSpark draft GGUF:
1. Rename dflash-draft.* keys to dflash.*
2. Rename nested dflash.dflash.* keys to dflash.* equivalents
3. Add tokenizer keys from K3 model
"""
import struct, sys, os
def read_gguf_kv(data, pos, n_kv):
"""Read all KV pairs, return list of (key_bytes, vtype, value_bytes_or_data)"""
kvs = []
for i in range(n_kv):
klen = struct.unpack_from("<Q", data, pos)[0]; pos += 8
key = data[pos:pos+klen]; pos += klen
vtype = struct.unpack_from("<I", data, pos)[0]; pos += 4
if vtype == 8: # string
vlen = struct.unpack_from("<Q", data, pos)[0]; pos += 8
val = data[pos:pos+vlen]; pos += vlen
kvs.append((key, vtype, val))
elif vtype in (0, 1): # u8, i8
val = data[pos:pos+1]; pos += 1
kvs.append((key, vtype, val))
elif vtype in (2, 3): # u16, i16
val = data[pos:pos+2]; pos += 2
kvs.append((key, vtype, val))
elif vtype in (4, 5, 6): # u32, i32, f32
val = data[pos:pos+4]; pos += 4
kvs.append((key, vtype, val))
elif vtype == 7: # bool
val = data[pos:pos+1]; pos += 1
kvs.append((key, vtype, val))
elif vtype in (10, 11, 12): # u64, i64, f64
val = data[pos:pos+8]; pos += 8
kvs.append((key, vtype, val))
elif vtype == 9: # array
atype = struct.unpack_from("<I", data, pos)[0]; pos += 4
alen = struct.unpack_from("<Q", data, pos)[0]; pos += 8
if atype == 8: # string array
vals = []
for j in range(alen):
slen = struct.unpack_from("<Q", data, pos)[0]; pos += 8
sval = data[pos:pos+slen]; pos += slen
vals.append(sval)
kvs.append((key, vtype, (atype, vals)))
elif atype in (0, 1, 7): # u8, i8, bool
val = data[pos:pos+alen]; pos += alen
kvs.append((key, vtype, (atype, val)))
elif atype in (2, 3): # u16, i16
nbytes = alen * 2
val = data[pos:pos+nbytes]; pos += nbytes
kvs.append((key, vtype, (atype, val)))
elif atype in (4, 5, 6): # u32, i32, f32
nbytes = alen * 4
val = data[pos:pos+nbytes]; pos += nbytes
kvs.append((key, vtype, (atype, val)))
elif atype in (10, 11, 12): # u64, i64, f64
nbytes = alen * 8
val = data[pos:pos+nbytes]; pos += nbytes
kvs.append((key, vtype, (atype, val)))
else:
raise ValueError(f"Unknown array type {atype}")
else:
raise ValueError(f"Unknown value type {vtype} for key {key}")
return kvs, pos
def write_gguf_kv(out, key, vtype, val):
"""Write one KV pair to output bytearray"""
out += struct.pack("<Q", len(key))
out += key
out += struct.pack("<I", vtype)
if vtype == 8: # string
out += struct.pack("<Q", len(val))
out += val
elif vtype in (0, 1, 7): # u8, i8, bool
out += val
elif vtype in (2, 3): # u16, i16
out += val
elif vtype in (4, 5, 6): # u32, i32, f32
out += val
elif vtype in (10, 11, 12): # u64, i64, f64
out += val
elif vtype == 9:
atype, data = val
if atype == 8: # string array
out += struct.pack("<I", atype)
out += struct.pack("<Q", len(data))
for s in data:
out += struct.pack("<Q", len(s))
out += s
else:
out += struct.pack("<I", atype)
if atype in (0, 1, 7):
alen = len(data)
elif atype in (2, 3):
alen = len(data) // 2
elif atype in (4, 5, 6):
alen = len(data) // 4
elif atype in (10, 11, 12):
alen = len(data) // 8
out += struct.pack("<Q", alen)
out += data
def main():
draft_src = "/root/models/k3-draft/original.gguf"
k3_src = "/root/models/Kimi-K3-GGUF/UD-Q4_K_XL/Kimi-K3-UD-Q4_K_XL-00001-of-00032.gguf"
dst = "/root/models/k3-draft/draft_final.gguf"
# Key renames for dflash-specific keys
KEY_RENAMES = {
b"dflash-draft.dflash.target_layer_ids": b"dflash.target_layers",
b"dflash-draft.dflash.block_size": b"dflash.block_size",
b"dflash-draft.dflash.n_target_layers": b"dflash.n_target_layers",
b"dflash-draft.dflash.n_target_features": b"dflash.n_target_features",
b"dflash-draft.dflash.target.block_count": b"dflash.target_block_count",
b"dflash-draft.dflash.mask_token_id": b"dflash.mask_token_id",
b"dflash-draft.dflash.target.repository": b"dflash.target_repository",
b"dflash-draft.dflash.dspark.enabled": b"dflash.dspark_enabled",
b"dflash-draft.dflash.dspark.markov_rank": b"dflash.markov_rank",
b"dflash-draft.dflash.dspark.vocab_size": b"dflash.dspark_vocab_size",
b"dflash-draft.dflash.dspark.markov_type": b"dflash.markov_type",
b"dflash-draft.dflash.dspark.confidence.enabled": b"dflash.confidence_enabled",
b"dflash-draft.dflash.dspark.confidence.with_markov": b"dflash.confidence_with_markov",
b"dflash-draft.dflash.dspark.confidence_dim": b"dflash.confidence_dim",
}
GENERAL_PREFIX_OLD = b"dflash-draft."
GENERAL_PREFIX_NEW = b"dflash."
# Read K3 tokenizer keys (stream - file too large to read into memory)
print("Reading K3 tokenizer keys...")
with open(k3_src, "rb") as f:
magic = f.read(4)
version = struct.unpack("<I", f.read(4))[0]
n_tensors_k3 = struct.unpack("<Q", f.read(8))[0]
n_kv_k3 = struct.unpack("<Q", f.read(8))[0]
k3_kvs = []
for i in range(n_kv_k3):
klen = struct.unpack("<Q", f.read(8))[0]
key = f.read(klen)
vtype = struct.unpack("<I", f.read(4))[0]
if vtype == 8:
vlen = struct.unpack("<Q", f.read(8))[0]
val = f.read(vlen)
k3_kvs.append((key, vtype, val))
elif vtype in (0, 1): # u8, i8
val = f.read(1)
k3_kvs.append((key, vtype, val))
elif vtype in (2, 3): # u16, i16
val = f.read(2)
k3_kvs.append((key, vtype, val))
elif vtype in (4, 5, 6): # u32, i32, f32
val = f.read(4)
k3_kvs.append((key, vtype, val))
elif vtype == 7: # bool
val = f.read(1)
k3_kvs.append((key, vtype, val))
elif vtype in (10, 11, 12): # u64, i64, f64
val = f.read(8)
k3_kvs.append((key, vtype, val))
elif vtype == 9:
atype = struct.unpack("<I", f.read(4))[0]
alen = struct.unpack("<Q", f.read(8))[0]
if atype == 8:
vals = []
for j in range(alen):
slen = struct.unpack("<Q", f.read(8))[0]
sval = f.read(slen)
vals.append(sval)
k3_kvs.append((key, vtype, (atype, vals)))
elif atype in (0, 1, 7): # u8, i8, bool
val = f.read(alen)
k3_kvs.append((key, vtype, (atype, val)))
elif atype in (2, 3): # u16, i16
val = f.read(alen * 2)
k3_kvs.append((key, vtype, (atype, val)))
elif atype in (4, 5, 6): # u32, i32, f32
val = f.read(alen * 4)
k3_kvs.append((key, vtype, (atype, val)))
elif atype in (10, 11, 12): # u64, i64, f64
val = f.read(alen * 8)
k3_kvs.append((key, vtype, (atype, val)))
tok_keys_needed = [
b"tokenizer.ggml.model",
b"tokenizer.ggml.pre",
b"tokenizer.ggml.tokens",
b"tokenizer.ggml.token_type",
b"tokenizer.ggml.merges",
]
tok_kvs = {}
for key, vtype, val in k3_kvs:
if key in tok_keys_needed:
tok_kvs[key] = (vtype, val)
if vtype == 8:
print(f" {key.decode()} = {val[:60]}... ({len(val)} bytes)")
elif vtype == 9:
atype, adata = val
print(f" {key.decode()} = [array type={atype} len={len(adata)}]")
else:
print(f" {key.decode()} = type {vtype}")
del k3_kvs # free after extracting tokenizer keys below
# Read draft GGUF
print("\nReading draft GGUF...")
with open(draft_src, "rb") as f:
data = f.read()
pos = 0
magic = data[pos:pos+4]; pos += 4
version = struct.unpack_from("<I", data, pos)[0]; pos += 4
n_tensors = struct.unpack_from("<Q", data, pos)[0]; pos += 8
n_kv = struct.unpack_from("<Q", data, pos)[0]; pos += 8
draft_kvs, kv_end = read_gguf_kv(data, pos, n_kv)
# Build new KV list: renamed draft keys + tokenizer keys
new_kvs = []
renamed = 0
existing_keys = set()
for key, vtype, val in draft_kvs:
if key in KEY_RENAMES:
new_key = KEY_RENAMES[key]
renamed += 1
print(f" rename: {key.decode()} -> {new_key.decode()}")
elif key.startswith(GENERAL_PREFIX_OLD):
new_key = GENERAL_PREFIX_NEW + key[len(GENERAL_PREFIX_OLD):]
renamed += 1
else:
new_key = key
new_kvs.append((new_key, vtype, val))
existing_keys.add(new_key)
# Add missing tokenizer keys
added = 0
for key in tok_keys_needed:
if key not in existing_keys and key in tok_kvs:
vtype, val = tok_kvs[key]
new_kvs.append((key, vtype, val))
added += 1
print(f" added: {key.decode()}")
print(f"\nRenamed {renamed} keys, added {added} tokenizer keys")
print(f"Total KV: {len(new_kvs)} (was {n_kv})")
# Write output
out = bytearray()
out += magic
out += struct.pack("<I", version)
out += struct.pack("<Q", n_tensors)
out += struct.pack("<Q", len(new_kvs))
for key, vtype, val in new_kvs:
write_gguf_kv(out, key, vtype, val)
# Read and rewrite tensor infos (rename tensors)
TENSOR_RENAMES = {
b"dflash.fc.weight": b"fc.weight",
b"dflash.hidden_norm.weight": b"enc.output_norm.weight",
b"dflash.dspark.confidence.weight": b"conf_proj.weight",
b"dflash.dspark.confidence.bias": b"conf_proj.bias",
b"dflash.dspark.markov.w1": b"markov_w1.weight",
b"dflash.dspark.markov.w2": b"markov_w2.weight",
}
tensor_infos_start = kv_end
pos = kv_end
tensor_infos_out = bytearray()
tensors_renamed = 0
for i in range(n_tensors):
tname_len = struct.unpack_from("<Q", data, pos)[0]; pos += 8
tname = data[pos:pos+tname_len]; pos += tname_len
n_dims = struct.unpack_from("<I", data, pos)[0]; pos += 4
dims = data[pos:pos+n_dims*8]; pos += n_dims * 8
dtype = data[pos:pos+4]; pos += 4
offset = data[pos:pos+8]; pos += 8
# Rename tensor if needed
if tname in TENSOR_RENAMES:
new_tname = TENSOR_RENAMES[tname]
tensors_renamed += 1
print(f" tensor rename: {tname.decode()} -> {new_tname.decode()}")
else:
new_tname = tname
tensor_infos_out += struct.pack("<Q", len(new_tname))
tensor_infos_out += new_tname
tensor_infos_out += struct.pack("<I", n_dims)
tensor_infos_out += dims
tensor_infos_out += dtype
tensor_infos_out += offset
tensor_infos_end = pos
print(f"Renamed {tensors_renamed} tensors")
alignment = 32
new_tensor_data_start = (len(out) + len(tensor_infos_out) + alignment - 1) // alignment * alignment
orig_tensor_data_start = (tensor_infos_end + alignment - 1) // alignment * alignment
out += tensor_infos_out
while len(out) < new_tensor_data_start:
out += b"\x00"
out += data[orig_tensor_data_start:]
with open(dst, "wb") as f:
f.write(out)
print(f"\nWrote {dst}: {len(out)} bytes (orig: {len(data)})")
if __name__ == "__main__":
main()
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