Instructions to use ukisai/Swift-1.5-5bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ukisai/Swift-1.5-5bit-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ukisai/Swift-1.5-5bit-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use ukisai/Swift-1.5-5bit-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 "ukisai/Swift-1.5-5bit-MLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ukisai/Swift-1.5-5bit-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use ukisai/Swift-1.5-5bit-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ukisai/Swift-1.5-5bit-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ukisai/Swift-1.5-5bit-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ukisai/Swift-1.5-5bit-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use ukisai/Swift-1.5-5bit-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 "ukisai/Swift-1.5-5bit-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 ukisai/Swift-1.5-5bit-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ukisai/Swift-1.5-5bit-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 "ukisai/Swift-1.5-5bit-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 "ukisai/Swift-1.5-5bit-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: 6,662 Bytes
e476358 | 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 | """Offline, bounded-memory file/index integrity check. Does not execute model code."""
import argparse
import hashlib
import json
import math
from pathlib import Path, PurePosixPath
import re
import struct
def unique(pairs):
result = {}
for key, value in pairs:
if key in result:
raise ValueError('duplicate JSON key')
result[key] = value
return result
def read_json(path):
return json.loads(path.read_text(encoding='utf-8'), object_pairs_hook=unique)
def safe_path(root, name):
if not isinstance(name, str) or not name or '\\' in name:
raise ValueError('unsafe file path')
p = PurePosixPath(name)
if p.is_absolute() or '..' in p.parts or str(p) != name:
raise ValueError('unsafe file path')
target = root.joinpath(*p.parts)
if any(parent.is_symlink() for parent in (target, *target.parents) if parent != root.parent):
raise ValueError('symlink path not allowed')
target.resolve().relative_to(root.resolve())
return target
def file_hashes(path):
size = path.stat().st_size
sha = hashlib.sha256()
blob = hashlib.sha1(f'blob {size}\0'.encode())
with path.open('rb') as stream:
for chunk in iter(lambda: stream.read(8 * 1024**2), b''):
sha.update(chunk)
blob.update(chunk)
return size, sha.hexdigest(), blob.hexdigest()
def tensor_header(path):
size = path.stat().st_size
with path.open('rb') as stream:
prefix = stream.read(8)
if len(prefix) != 8:
raise ValueError('short safetensors prefix')
n = struct.unpack('<Q', prefix)[0]
if n > 16 * 1024**2 or 8+n > size:
raise ValueError('invalid header boundary')
header = json.loads(stream.read(n), object_pairs_hook=unique)
widths = {'BF16': 2, 'F16': 2, 'F32': 4, 'F64': 8, 'U32': 4, 'I32': 4,
'U8': 1, 'I8': 1, 'U16': 2, 'I16': 2, 'U64': 8, 'I64': 8, 'BOOL': 1}
spans, names = [], set()
for name, value in header.items():
if name == '__metadata__':
continue
shape = value['shape']
if not isinstance(shape, list) or any(type(x) is not int or x < 0 for x in shape):
raise ValueError('invalid tensor shape')
start, end = value['data_offsets']
if type(start) is not int or type(end) is not int or not 0 <= start <= end <= size-8-n:
raise ValueError('invalid tensor payload boundary')
if end-start != math.prod(shape)*widths[value['dtype']]:
raise ValueError('dtype/shape byte count mismatch')
spans.append((start, end))
names.add(name)
cursor = 0
for start, end in sorted(spans):
if start != cursor:
raise ValueError('payload gap or overlap')
cursor = end
if cursor != size-8-n:
raise ValueError('unreferenced or truncated payload')
return names
def verify(root, expected_manifest_sha256=None):
root = Path(root).absolute()
errors, checked = [], []
try:
manifest_path = safe_path(root, 'UPLOAD_MANIFEST.json')
_, manifest_sha, _ = file_hashes(manifest_path)
if expected_manifest_sha256 and expected_manifest_sha256 != manifest_sha:
raise ValueError('trusted manifest hash mismatch')
manifest = read_json(manifest_path)
entries = manifest['files']
names = [e['path'] for e in entries]
if len(names) != len(set(names)):
raise ValueError('duplicate manifest file')
if any(type(e['bytes']) is not int or e['bytes'] < 0 for e in entries):
raise ValueError('invalid manifest size')
if len(entries) != manifest['file_count'] or sum(e['bytes'] for e in entries) != manifest['total_bytes']:
raise ValueError('manifest aggregates mismatch')
for e in entries:
if e['path'] == 'UPLOAD_MANIFEST.json' or not re.fullmatch('[a-f0-9]{64}', e['sha256']):
raise ValueError('invalid manifest entry')
p = safe_path(root, e['path'])
if not p.is_file():
errors.append({'file': e['path'], 'reason': 'missing file'})
continue
size, sha, blob = file_hashes(p)
if size != e['bytes'] or sha != e['sha256'] or (e.get('git_blob_sha1') and blob != e['git_blob_sha1']):
errors.append({'file': e['path'], 'reason': 'size or full-file hash mismatch'})
continue
if p.suffix == '.json':
read_json(p)
checked.append(e['path'])
index_name = 'model.safetensors.index.json'
if index_name not in names:
errors.append({'file': index_name, 'reason': 'required index not in manifest'})
elif index_name in checked:
index = read_json(root/index_name)['weight_map']
if not isinstance(index, dict) or not index:
raise ValueError('invalid weight map')
shards = set(index.values())
expected_shards = {n for n in names if n.endswith('.safetensors')}
if shards != expected_shards:
errors.append({'reason': 'index/manifest shard set mismatch'})
actual = {}
for shard in sorted(shards):
safe_path(root, shard)
if shard not in checked:
errors.append({'file': shard, 'reason': 'referenced shard missing or not hash-verified'})
continue
for tensor in tensor_header(root/shard):
if tensor in actual:
raise ValueError('duplicate tensor across shards')
actual[tensor] = shard
if actual != index:
errors.append({'reason': 'actual tensor inventory differs from weight map'})
except (OSError, ValueError, TypeError, KeyError, AttributeError) as exc:
errors.append({'reason': 'invalid or incomplete package', 'exception_type': type(exc).__name__})
return {'status': 'FAIL' if errors else 'PASS', 'errors': errors,
'fully_hashed_files': len(checked), 'manifest_pinned': bool(expected_manifest_sha256),
'scope': 'File integrity and tensor index only; not generation, finite-value or quality validation'}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('snapshot', type=Path)
parser.add_argument('--manifest-sha256')
args = parser.parse_args()
result = verify(args.snapshot, args.manifest_sha256)
print(json.dumps(result, indent=2))
raise SystemExit(0 if result['status'] == 'PASS' else 1)
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