Image-Text-to-Text
MLX
Safetensors
step3p7
Mixture of Experts
vision-language
pruned
reap
quantized
conversational
custom_code
4-bit precision
Instructions to use True2456/Mati-3.7-173B-4.6bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use True2456/Mati-3.7-173B-4.6bit-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("True2456/Mati-3.7-173B-4.6bit-MLX") config = load_config("True2456/Mati-3.7-173B-4.6bit-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use True2456/Mati-3.7-173B-4.6bit-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "True2456/Mati-3.7-173B-4.6bit-MLX"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "True2456/Mati-3.7-173B-4.6bit-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use True2456/Mati-3.7-173B-4.6bit-MLX with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "True2456/Mati-3.7-173B-4.6bit-MLX"
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 True2456/Mati-3.7-173B-4.6bit-MLX
Run Hermes
hermes
- OpenClaw new
How to use True2456/Mati-3.7-173B-4.6bit-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "True2456/Mati-3.7-173B-4.6bit-MLX"
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 "True2456/Mati-3.7-173B-4.6bit-MLX" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 7,207 Bytes
31b4aff | 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 | #!/usr/bin/env python3
"""Catch Step-3.7's numeric corruption before a tool call executes.
Meant to be copied into an agent harness as a pre-tool-use check, not run as
part of the REAP pipeline. See the HF README's "numbers corrupted inside the
reasoning block" section for the measured behaviour this defends against.
Why code and not the model: the failure survives self-review. In one traced
run the model re-read the question 20+ times and reaffirmed the wrong value
each time, because by then its own output was the strongest evidence in
context. A checker that does not share that context does not share the bias.
Three checks, cheapest first:
scan_signature(text) known corruption shapes -- 4.4.7, "1 8 4 5",
2,4,5,6,1,3,3,7. No source needed.
check_provenance(cmd,src) every numeral in cmd must appear in src. Catches
invented numbers that happen to look well-formed.
check_sed_bounds(cmd) sed line ranges against the real file length.
Usage:
from numeric_guard import guard
problems = guard(proposed_command, source_text=file_contents)
if problems: ...retry instead of executing...
$ python3 scripts/numeric_guard.py "sed -n '4.4.7,4.9.5p' src/aero.py"
"""
from __future__ import annotations
import re
import sys
from pathlib import Path
# 1.2.0 / 4.4.7 -- a numeral carrying more interior dots than a decimal can.
# Version-like strings are legitimate in many contexts, so this is reported
# rather than treated as certainly wrong; see guard()'s `strict` flag.
MULTI_DOT = re.compile(r"(?<![\w.])\d+\.\d+\.\d+(?![\w.])")
# "1 8 4 5" / "2 4 5 6" -- three or more single digits separated by spaces.
SPACED_DIGITS = re.compile(r"(?<!\d)\d(?: \d){2,}(?!\d)")
# "2,4,5,6,1,3,3,7" -- single digits comma-separated. Distinguished from a
# real list like "2456, 1337" by every element being exactly one digit.
COMMA_DIGITS = re.compile(r"(?<!\d)\d(?:,\d){3,}(?!\d)")
NUMERALS = re.compile(r"\d+")
SED_RANGE = re.compile(r"sed\s+-n\s+['\"]?(\d+),(\d+)p['\"]?\s+(\S+)")
# 5_1_8_4_0_0 -- underscore inserted between single digits. Python's numeric
# separators make `_` a legitimate delimiter, which primes exactly this. Note
# `5_1_8_4_0_0.0 == 518400.0` is True, so in Python this is cosmetic rather
# than wrong -- but it is a hard error in JSON, YAML and shell.
# Trailing `.` must be allowed -- these appear as float literals (5_1_8_4_0_0.0).
UNDERSCORE_NUM = re.compile(r"(?<![\w.])\d[\d_]*_[\d_]*\d(?!\w)")
def _bad_underscore_grouping(lit: str) -> bool:
"""True unless the literal uses conventional 3-digit grouping.
Legitimate: 5_000_000, 1_234, 12_345_678 -- every group after the first is
exactly 3 digits and the first is 1-3. Anything else (5_1_8_4, 1_00_000)
is the corruption signature.
"""
groups = lit.split("_")
if any(g == "" for g in groups):
return True
return not (1 <= len(groups[0]) <= 3 and all(len(g) == 3 for g in groups[1:]))
def scan_signature(text: str, strict: bool = False) -> list[str]:
"""Known corruption shapes. No source text required."""
out = []
for m in SPACED_DIGITS.finditer(text):
out.append(f"digits split by spaces: {m.group(0)!r}")
for m in COMMA_DIGITS.finditer(text):
out.append(f"digits split by commas: {m.group(0)!r}")
for m in UNDERSCORE_NUM.finditer(text):
if _bad_underscore_grouping(m.group(0)):
out.append(f"digits split by underscores: {m.group(0)!r} "
f"(valid Python, but wrong in JSON/YAML/shell)")
for m in MULTI_DOT.finditer(text):
label = "malformed number" if strict else "version-like numeral (check)"
out.append(f"{label}: {m.group(0)!r}")
return out
def check_provenance(command: str, source: str, min_len: int = 3) -> list[str]:
"""Every numeral of >=min_len digits in `command` must occur in `source`.
min_len avoids flagging small incidental numbers (-9, exit codes, 0/1).
Numbers the model legitimately *computed* will also trip this, so treat
hits as "confirm before running", not as proof of corruption.
"""
return [f"numeral {n!r} does not appear in the source"
for n in {m.group(0) for m in NUMERALS.finditer(command)}
if len(n) >= min_len and n not in source]
def check_sed_bounds(command: str, root: str | Path = ".") -> list[str]:
"""sed line ranges against the file's real length."""
out = []
for start, end, path in SED_RANGE.findall(command):
p = Path(root) / path
if not p.exists():
continue
n = sum(1 for _ in p.open(errors="replace"))
s, e = int(start), int(end)
if s > e:
out.append(f"sed range {s},{e} is inverted")
if s > n or e > n:
out.append(f"sed range {s},{e} exceeds {path} ({n} lines)")
return out
def check_python_int_positions(code: str) -> list[str]:
"""Float literals where Python requires an int: slice indices, range().
This is the corruption's most dangerous form, because one inserted '.'
yields a *valid* float that no shape-based check can distinguish from a
legitimate one. `content[idx-5:idx+1.5]` parses fine and fails only at
runtime with "slice indices must be integers" -- the exact loop seen in
the wild. Catching it needs the syntactic position, not the literal.
"""
import ast
try:
tree = ast.parse(code)
except SyntaxError:
return []
out = []
def floats_in(node):
return [n for n in ast.walk(node)
if isinstance(n, ast.Constant) and isinstance(n.value, float)]
for node in ast.walk(tree):
if isinstance(node, ast.Subscript):
for f in floats_in(node.slice):
out.append(f"float {f.value!r} used as a slice index "
f"(line {f.lineno}) -- fails at runtime")
elif isinstance(node, ast.Call) and getattr(node.func, "id", "") == "range":
for arg in node.args:
for f in floats_in(arg):
out.append(f"float {f.value!r} passed to range() "
f"(line {f.lineno}) -- fails at runtime")
return out
def guard(command: str, source_text: str | None = None,
root: str | Path = ".", strict: bool = False,
as_python: bool = False) -> list[str]:
"""All applicable checks. Empty list means nothing suspicious."""
problems = scan_signature(command, strict=strict)
if source_text is not None:
problems += check_provenance(command, source_text)
problems += check_sed_bounds(command, root)
if as_python:
problems += check_python_int_positions(command)
return problems
if __name__ == "__main__":
if len(sys.argv) < 2:
print(__doc__.strip().split("Usage:")[-1].strip())
raise SystemExit(2)
cmd = sys.argv[1]
src = Path(sys.argv[2]).read_text() if len(sys.argv) > 2 else None
found = guard(cmd, source_text=src)
if not found:
print("ok")
else:
print(f"SUSPECT: {cmd!r}")
for p in found:
print(f" - {p}")
raise SystemExit(1)
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