Instructions to use hipinis/20260718 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 hipinis/20260718 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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718: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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf hipinis/20260718: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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf hipinis/20260718:Q8_0
Use Docker
docker model run hf.co/hipinis/20260718:Q8_0
- LM Studio
- Jan
- Ollama
How to use hipinis/20260718 with Ollama:
ollama run hf.co/hipinis/20260718:Q8_0
- Unsloth Desktop
- Pi
How to use hipinis/20260718 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718: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": "hipinis/20260718:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use hipinis/20260718 with Docker Model Runner:
docker model run hf.co/hipinis/20260718:Q8_0
- Lemonade
How to use hipinis/20260718 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hipinis/20260718:Q8_0
Run and chat with the model
lemonade run user.20260718-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use hipinis/20260718 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718: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 hipinis/20260718:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hipinis/20260718 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718: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 "hipinis/20260718: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: 5,969 Bytes
96d97a7 | 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 | import re
_INLINE_TAG_RE = re.compile(r"\[([\d\.]+)(?:[:\-]([\d\.]+))?\]")
_DIGIT_RANGE_TAIL_RE = re.compile(r"([\d]+(?:\.\d+)?)\s*[-\u2013]\s*([\d]+(?:\.\d+)?)\s*$")
def _try_parse_num(s):
"""Parse s as integer. Tries digit conversion first, then word2number if available."""
s = s.strip()
try:
return int(float(s))
except (ValueError, TypeError):
pass
try:
from word2number import w2n
return int(w2n.word_to_num(s))
except Exception:
pass
return None
def _parse_header(line):
"""Return (start, end_or_None) if line is a block segment header, else None.
Valid format: one or more prefix words, followed by a number (digit or word form),
optional range, then colon at end of line. Matching is case-insensitive.
Examples:
'Scene 1:' -> (1, None)
'My Scene 3:' -> (3, None)
'Shot 2-4:' -> (2, 4)
'Part eleven:' -> (11, None) [requires word2number]
'segment twenty:' -> (20, None) [requires word2number]
"""
line = line.strip()
if not line.endswith(':'):
return None
body = line[:-1].rstrip()
tokens = body.split()
# Need at least 2 tokens: at least one prefix word + one number token
if len(tokens) < 2:
return None
# Try digit range at end: "Scene 2-4:"
m = _DIGIT_RANGE_TAIL_RE.search(body)
if m and body[:m.start()].strip():
start = _try_parse_num(m.group(1))
end = _try_parse_num(m.group(2))
if start is not None and end is not None:
return (start, end)
# Try 1..N tail tokens as word-or-digit number (longest candidate first)
# Keep at least one prefix token so bare "1:" is not matched as a header
max_num_tokens = min(4, len(tokens) - 1)
for n in range(max_num_tokens, 0, -1):
candidate = " ".join(tokens[-n:])
val = _try_parse_num(candidate)
if val is not None:
return (val, None)
return None
def _extract_inline_tag(text):
"""Extract first [n] or [n-m] weight tag from text.
Returns (clean_text, weight_or_None). Tag is stripped from text.
"""
m = _INLINE_TAG_RE.search(text)
if not m:
return text.strip(), None
val1 = float(m.group(1))
val2 = float(m.group(2)) if m.group(2) else None
weight = (val2 - val1) if val2 is not None else val1
clean = _INLINE_TAG_RE.sub("", text).strip()
return clean, weight
def _parse_inline_syntax(text):
"""Parse pipe-separated inline syntax with optional [n-m] weight tags.
Syntax examples:
'one | two | three' -> equal weights
'one [0-50] | two [50-150] | three [150]' -> proportional weights
"""
segments = []
for part in text.split('|'):
clean, weight = _extract_inline_tag(part)
if clean:
segments.append({"text": clean, "weight": weight if weight is not None else 1.0})
return segments
def _parse_block_syntax(text):
"""Parse block header syntax where each segment is preceded by a header line.
Header format: any words followed by a number (or word-number) and a colon
on its own line. Optional [n-m] inline tag in body overrides header weight.
Syntax examples:
'Scene 1:\\ntext\\nScene 2:\\ntext'
'My Part 3-6:\\ntext' -> weight = 6-3 = 3
'segment eleven:\\ntext' -> weight = 1.0 (single number = sequence marker)
"""
lines = text.splitlines(keepends=True)
raw_segments = []
current_header = None
current_body = []
for line in lines:
h = _parse_header(line)
if h is not None:
if current_body or current_header is not None:
raw_segments.append((current_header, "".join(current_body)))
current_header = h
current_body = []
else:
current_body.append(line)
if current_body or current_header is not None:
raw_segments.append((current_header, "".join(current_body)))
segments = []
for header, body in raw_segments:
clean, inline_weight = _extract_inline_tag(body)
if not clean:
continue
if inline_weight is not None:
weight = inline_weight
elif header is not None:
start, end = header
# Range header: weight is proportional span. Single number: equal weight.
weight = (end - start) if end is not None else 1.0
else:
weight = 1.0
segments.append({"text": clean, "weight": weight})
return segments
def parse_smart_prompt(text):
"""Parse smart_prompt text into a list of {"text": str, "weight": float} dicts.
Detects syntax automatically:
--- Inline (newline-agnostic) ---
Segments separated by | with optional [n-m] proportional weight tags.
'man walks | man runs | man jumps'
'man walks [0-50] | man runs [50-150] | man jumps [150-200]'
--- Block (newline-specific) ---
Segments preceded by a header line: any words + number + colon on its own line.
The number (or range) is stripped and used only for weight. Ordinal numbers
like 'Scene 1:' are sequence markers (equal weight). Ranges like 'Scene 1-3:'
assign proportional weight (3-1=2). Word-form numbers require word2number package.
'Scene 1:\\nman walks\\nScene 2:\\nman runs'
'My Shot 1-3:\\nman walks\\nMy Shot 3-7:\\nman runs'
'segment eleven:\\nman walks' (requires: pip install word2number)
All syntax markers are fully stripped before text is returned.
"""
lines = text.splitlines()
has_blocks = any(_parse_header(line) is not None for line in lines)
if has_blocks:
return _parse_block_syntax(text)
return _parse_inline_syntax(text)
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