Instructions to use appvoid/void.0 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 appvoid/void.0 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 appvoid/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf appvoid/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.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 appvoid/void.0 # Run inference directly in the terminal: ./llama-cli -hf appvoid/void.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 appvoid/void.0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/void.0
Use Docker
docker model run hf.co/appvoid/void.0
- LM Studio
- Jan
- Ollama
How to use appvoid/void.0 with Ollama:
ollama run hf.co/appvoid/void.0
- Unsloth Studio
How to use appvoid/void.0 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for appvoid/void.0 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for appvoid/void.0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for appvoid/void.0 to start chatting
- Docker Model Runner
How to use appvoid/void.0 with Docker Model Runner:
docker model run hf.co/appvoid/void.0
- Lemonade
How to use appvoid/void.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/void.0
Run and chat with the model
lemonade run user.void.0-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Continued pretraining: FineWeb-Edu + rewrite6 byte hybrid + no-prompt-15k
Browse files- hybrid_tokenizer.py +154 -0
- hybrid_tokenizer_config.json +301 -0
hybrid_tokenizer.py
ADDED
|
@@ -0,0 +1,154 @@
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| 1 |
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from __future__ import annotations
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| 2 |
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|
| 3 |
+
from typing import Iterable
|
| 4 |
+
from transformers import AutoTokenizer
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class HybridByteTokenizer:
|
| 8 |
+
def __init__(self, model_id_or_path: str, **kwargs):
|
| 9 |
+
self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path, **kwargs)
|
| 10 |
+
vocab = self.tokenizer.get_vocab()
|
| 11 |
+
|
| 12 |
+
self.byte_token_ids = []
|
| 13 |
+
for value in range(256):
|
| 14 |
+
token = f"<0x{value:02X}>"
|
| 15 |
+
if token not in vocab:
|
| 16 |
+
raise ValueError(f"Missing byte token: {token}")
|
| 17 |
+
self.byte_token_ids.append(int(vocab[token]))
|
| 18 |
+
|
| 19 |
+
self.byte_id_to_value = {
|
| 20 |
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token_id: value
|
| 21 |
+
for value, token_id in enumerate(self.byte_token_ids)
|
| 22 |
+
}
|
| 23 |
+
self.separator_ids = self.tokenizer.encode(
|
| 24 |
+
"\\n\\n", add_special_tokens=False
|
| 25 |
+
)
|
| 26 |
+
self.quote_ids = self.tokenizer.encode(
|
| 27 |
+
'"', add_special_tokens=False
|
| 28 |
+
)
|
| 29 |
+
|
| 30 |
+
def encode_bytes(self, text: str) -> list[int]:
|
| 31 |
+
return [
|
| 32 |
+
self.byte_token_ids[value]
|
| 33 |
+
for value in str(text or "").encode("utf-8")
|
| 34 |
+
]
|
| 35 |
+
|
| 36 |
+
def encode_rewrite(
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| 37 |
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self,
|
| 38 |
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instruction: str,
|
| 39 |
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text: str,
|
| 40 |
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output: str = "",
|
| 41 |
+
) -> list[int]:
|
| 42 |
+
ids = []
|
| 43 |
+
instruction = str(instruction or "").strip()
|
| 44 |
+
|
| 45 |
+
if instruction:
|
| 46 |
+
ids.extend(
|
| 47 |
+
self.tokenizer.encode(
|
| 48 |
+
instruction,
|
| 49 |
+
add_special_tokens=False,
|
| 50 |
+
)
|
| 51 |
+
)
|
| 52 |
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ids.extend(self.separator_ids)
|
| 53 |
+
|
| 54 |
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ids.extend(self.quote_ids)
|
| 55 |
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ids.extend(self.encode_bytes(text))
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| 56 |
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ids.extend(self.quote_ids)
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| 57 |
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ids.extend(self.separator_ids)
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| 58 |
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ids.extend(self.quote_ids)
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| 59 |
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ids.extend(self.encode_bytes(output))
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| 60 |
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ids.extend(self.quote_ids)
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| 61 |
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return ids
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| 62 |
+
|
| 63 |
+
def encode_text(
|
| 64 |
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self,
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| 65 |
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text: str,
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| 66 |
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byte_inside_quotes: bool = True,
|
| 67 |
+
) -> list[int]:
|
| 68 |
+
if not byte_inside_quotes:
|
| 69 |
+
return self.tokenizer.encode(text, add_special_tokens=False)
|
| 70 |
+
|
| 71 |
+
ids = []
|
| 72 |
+
normal = []
|
| 73 |
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quoted = []
|
| 74 |
+
inside = False
|
| 75 |
+
escaped = False
|
| 76 |
+
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| 77 |
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def flush_normal():
|
| 78 |
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if normal:
|
| 79 |
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ids.extend(
|
| 80 |
+
self.tokenizer.encode(
|
| 81 |
+
"".join(normal),
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| 82 |
+
add_special_tokens=False,
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| 83 |
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)
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| 84 |
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)
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| 85 |
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normal.clear()
|
| 86 |
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| 87 |
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def flush_quoted():
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| 88 |
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if quoted:
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| 89 |
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ids.extend(self.encode_bytes("".join(quoted)))
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| 90 |
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quoted.clear()
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| 91 |
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| 92 |
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for character in str(text):
|
| 93 |
+
if inside:
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| 94 |
+
if character == '"' and not escaped:
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| 95 |
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flush_quoted()
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| 96 |
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ids.extend(self.quote_ids)
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| 97 |
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inside = False
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| 98 |
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escaped = False
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| 99 |
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continue
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| 100 |
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quoted.append(character)
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| 101 |
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escaped = character == "\\\\" and not escaped
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| 102 |
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if character != "\\\\":
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| 103 |
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escaped = False
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| 104 |
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else:
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| 105 |
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if character == '"':
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| 106 |
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flush_normal()
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| 107 |
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ids.extend(self.quote_ids)
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| 108 |
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inside = True
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| 109 |
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else:
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| 110 |
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normal.append(character)
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| 111 |
+
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| 112 |
+
if inside:
|
| 113 |
+
flush_quoted()
|
| 114 |
+
else:
|
| 115 |
+
flush_normal()
|
| 116 |
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return ids
|
| 117 |
+
|
| 118 |
+
def decode(self, token_ids: Iterable[int]) -> str:
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| 119 |
+
output = []
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| 120 |
+
normal = []
|
| 121 |
+
byte_buffer = bytearray()
|
| 122 |
+
|
| 123 |
+
def flush_normal():
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| 124 |
+
if normal:
|
| 125 |
+
output.append(
|
| 126 |
+
self.tokenizer.decode(
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| 127 |
+
normal,
|
| 128 |
+
skip_special_tokens=False,
|
| 129 |
+
clean_up_tokenization_spaces=False,
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| 130 |
+
)
|
| 131 |
+
)
|
| 132 |
+
normal.clear()
|
| 133 |
+
|
| 134 |
+
def flush_bytes():
|
| 135 |
+
if byte_buffer:
|
| 136 |
+
output.append(
|
| 137 |
+
byte_buffer.decode("utf-8", errors="replace")
|
| 138 |
+
)
|
| 139 |
+
byte_buffer.clear()
|
| 140 |
+
|
| 141 |
+
for token_id_raw in token_ids:
|
| 142 |
+
token_id = int(token_id_raw)
|
| 143 |
+
byte_value = self.byte_id_to_value.get(token_id)
|
| 144 |
+
|
| 145 |
+
if byte_value is None:
|
| 146 |
+
flush_bytes()
|
| 147 |
+
normal.append(token_id)
|
| 148 |
+
else:
|
| 149 |
+
flush_normal()
|
| 150 |
+
byte_buffer.append(byte_value)
|
| 151 |
+
|
| 152 |
+
flush_bytes()
|
| 153 |
+
flush_normal()
|
| 154 |
+
return "".join(output)
|
hybrid_tokenizer_config.json
ADDED
|
@@ -0,0 +1,301 @@
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|
| 1 |
+
{
|
| 2 |
+
"base_model": "appvoid/void.0",
|
| 3 |
+
"byte_token_template": "<0x{:02X}>",
|
| 4 |
+
"byte_tokens": [
|
| 5 |
+
"<0x00>",
|
| 6 |
+
"<0x01>",
|
| 7 |
+
"<0x02>",
|
| 8 |
+
"<0x03>",
|
| 9 |
+
"<0x04>",
|
| 10 |
+
"<0x05>",
|
| 11 |
+
"<0x06>",
|
| 12 |
+
"<0x07>",
|
| 13 |
+
"<0x08>",
|
| 14 |
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| 15 |
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| 16 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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],
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}
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