Text Generation
Transformers
Safetensors
MLX
code
llama
fill-in-the-middle
multi-token-prediction
speculative-decoding
apple-silicon
text-generation-inference
Instructions to use philipjohnbasile/wisp-coder-110m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philipjohnbasile/wisp-coder-110m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="philipjohnbasile/wisp-coder-110m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("philipjohnbasile/wisp-coder-110m") model = AutoModelForCausalLM.from_pretrained("philipjohnbasile/wisp-coder-110m", device_map="auto") - MLX
How to use philipjohnbasile/wisp-coder-110m with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("philipjohnbasile/wisp-coder-110m") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use philipjohnbasile/wisp-coder-110m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "philipjohnbasile/wisp-coder-110m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "philipjohnbasile/wisp-coder-110m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/philipjohnbasile/wisp-coder-110m
- SGLang
How to use philipjohnbasile/wisp-coder-110m with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "philipjohnbasile/wisp-coder-110m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "philipjohnbasile/wisp-coder-110m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "philipjohnbasile/wisp-coder-110m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "philipjohnbasile/wisp-coder-110m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use philipjohnbasile/wisp-coder-110m with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "philipjohnbasile/wisp-coder-110m" --prompt "Once upon a time"
- Docker Model Runner
How to use philipjohnbasile/wisp-coder-110m with Docker Model Runner:
docker model run hf.co/philipjohnbasile/wisp-coder-110m
- Atomic Chat
File size: 16,395 Bytes
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import ctypes
import errno
import hashlib
import json
import math
import os
import re
import secrets
import shutil
import stat
import sys
RENAME_SWAP = 0x00000002
RENAME_EXCL = 0x00000004
CHECKPOINT_FILENAMES = (
"master.safetensors",
"meta.json",
"optimizer.safetensors",
)
def _require_real_directory(path, label):
if os.path.islink(path) or not os.path.isdir(path):
raise ValueError(f"{label} is not a real directory: {path}")
def _fsync_directory(path):
descriptor = os.open(path, os.O_RDONLY)
try:
os.fsync(descriptor)
finally:
os.close(descriptor)
def _same_identity(left, right):
return (
left.st_dev,
left.st_ino,
left.st_mode,
left.st_size,
left.st_mtime_ns,
left.st_ctime_ns,
) == (
right.st_dev,
right.st_ino,
right.st_mode,
right.st_size,
right.st_mtime_ns,
right.st_ctime_ns,
)
def _stable_regular_file(path, label, capture_bytes=False):
before_path = os.lstat(path)
if not stat.S_ISREG(before_path.st_mode):
raise ValueError(f"{label} is not a real regular file: {path}")
flags = os.O_RDONLY | getattr(os, "O_NOFOLLOW", 0)
descriptor = os.open(path, flags)
digest = hashlib.sha256()
blocks = [] if capture_bytes else None
try:
before_fd = os.fstat(descriptor)
if not stat.S_ISREG(before_fd.st_mode) or not _same_identity(
before_path, before_fd
):
raise ValueError(f"{label} changed while it was opened: {path}")
while True:
block = os.read(descriptor, 1024 * 1024)
if not block:
break
digest.update(block)
if blocks is not None:
blocks.append(block)
after_fd = os.fstat(descriptor)
finally:
os.close(descriptor)
after_path = os.lstat(path)
if not (
_same_identity(before_fd, after_fd)
and _same_identity(before_fd, after_path)
):
raise ValueError(f"{label} changed while it was read: {path}")
return {
"mode": stat.S_IMODE(before_fd.st_mode),
"size": before_fd.st_size,
"sha256": digest.hexdigest(),
"bytes": b"".join(blocks) if blocks is not None else None,
}
def _reject_duplicate_object_keys(pairs):
value = {}
for key, item in pairs:
if key in value:
raise ValueError(f"duplicate JSON key: {key}")
value[key] = item
return value
def _finite_json_float(raw):
value = float(raw)
if not math.isfinite(value):
raise ValueError(f"non-finite JSON number: {raw}")
return value
def _parse_checkpoint_meta(raw, label):
try:
value = json.loads(
raw,
object_pairs_hook=_reject_duplicate_object_keys,
parse_float=_finite_json_float,
parse_constant=lambda token: (_ for _ in ()).throw(
ValueError(f"non-finite JSON number: {token}")
),
)
except (UnicodeDecodeError, json.JSONDecodeError, ValueError) as exc:
raise ValueError(f"{label} is not strict JSON") from exc
legacy_fields = {
"step",
"config",
"model_args",
"optimizer_state_included",
}
exact_fields = set(value) if isinstance(value, dict) else set()
if exact_fields not in (legacy_fields, legacy_fields | {"train_sampler"}):
raise ValueError(f"{label} has an unknown root field set")
if (
not isinstance(value["step"], int)
or isinstance(value["step"], bool)
or value["step"] <= 0
or not isinstance(value["config"], dict)
or not isinstance(value["model_args"], dict)
or value["optimizer_state_included"] is not True
or (
"train_sampler" in value
and not isinstance(value["train_sampler"], dict)
)
):
raise ValueError(f"{label} has an invalid training checkpoint shape")
return value
def _checkpoint_manifest(path, label):
before = os.lstat(path)
if not stat.S_ISDIR(before.st_mode):
raise ValueError(f"{label} is not a real directory: {path}")
names = tuple(sorted(os.listdir(path)))
if names != tuple(sorted(CHECKPOINT_FILENAMES)):
raise ValueError(f"{label} does not have the exact checkpoint file set")
files = {}
metadata = None
for name in CHECKPOINT_FILENAMES:
result = _stable_regular_file(
os.path.join(path, name),
f"{label} {name}",
capture_bytes=name == "meta.json",
)
files[name] = {
"mode": result["mode"],
"size": result["size"],
"sha256": result["sha256"],
}
if name == "meta.json":
metadata = _parse_checkpoint_meta(
result["bytes"],
f"{label} meta.json",
)
after = os.lstat(path)
if not _same_identity(before, after):
raise ValueError(f"{label} changed while it was inspected: {path}")
return {"files": files, "metadata": metadata}
def _copy_regular_file(source, destination, label):
source_flags = os.O_RDONLY | getattr(os, "O_NOFOLLOW", 0)
source_descriptor = os.open(source, source_flags)
destination_descriptor = None
try:
source_stat = os.fstat(source_descriptor)
if not stat.S_ISREG(source_stat.st_mode):
raise ValueError(f"{label} source is not a regular file")
destination_descriptor = os.open(
destination,
os.O_WRONLY | os.O_CREAT | os.O_EXCL,
stat.S_IMODE(source_stat.st_mode),
)
os.fchmod(destination_descriptor, stat.S_IMODE(source_stat.st_mode))
while True:
block = os.read(source_descriptor, 1024 * 1024)
if not block:
break
offset = 0
while offset < len(block):
offset += os.write(destination_descriptor, block[offset:])
os.fsync(destination_descriptor)
finally:
if destination_descriptor is not None:
os.close(destination_descriptor)
os.close(source_descriptor)
def _rename_directory_no_replace(staging, target):
if sys.platform != "darwin":
raise RuntimeError(
"exclusive checkpoint directory publication requires macOS"
)
libc = ctypes.CDLL(None, use_errno=True)
renamex_np = libc.renamex_np
renamex_np.argtypes = [
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.c_uint,
]
renamex_np.restype = ctypes.c_int
result = renamex_np(
os.fsencode(staging),
os.fsencode(target),
RENAME_EXCL,
)
if result != 0:
error = ctypes.get_errno()
raise OSError(error, os.strerror(error), f"{staging} -> {target}")
def _remove_snapshot_recovery_staging(staging):
"""Remove only a private staging tree with checkpoint-shaped contents."""
_require_real_directory(staging, "snapshot recovery staging")
names = tuple(sorted(os.listdir(staging)))
unexpected = set(names) - set(CHECKPOINT_FILENAMES)
if unexpected:
raise ValueError(
"snapshot recovery staging contains an unowned entry: "
+ sorted(unexpected)[0]
)
for name in names:
path = os.path.join(staging, name)
info = os.lstat(path)
if not stat.S_ISREG(info.st_mode):
raise ValueError(
"snapshot recovery staging contains a non-file entry: "
+ name
)
for name in names:
os.unlink(os.path.join(staging, name))
os.rmdir(staging)
def _create_snapshot_recovery_staging(snapshot_checkpoint):
"""Create a private, invocation-owned sibling staging directory."""
parent = os.path.dirname(snapshot_checkpoint)
basename = os.path.basename(snapshot_checkpoint)
for _ in range(128):
token = secrets.token_hex(16)
staging = os.path.join(
parent,
f".{basename}.recovery-{token}.partial",
)
try:
os.mkdir(staging, 0o700)
except FileExistsError:
continue
os.chmod(staging, 0o700)
return staging
raise RuntimeError("could not allocate private snapshot recovery staging")
def ensure_snapshot_checkpoint(
resume_checkpoint,
snapshot_checkpoint,
expected_step,
expected_metadata,
):
"""
Make a missing immortal snapshot an exact copy of the resume checkpoint.
A differing existing snapshot is evidence from another training state and
is never replaced. Publication is exclusive, so even a racing creator
cannot be overwritten.
"""
if (
not isinstance(expected_step, int)
or isinstance(expected_step, bool)
or expected_step <= 0
or not isinstance(expected_metadata, dict)
):
raise ValueError("snapshot recovery inputs are malformed")
resume_checkpoint = os.path.abspath(resume_checkpoint)
snapshot_checkpoint = os.path.abspath(snapshot_checkpoint)
if resume_checkpoint == snapshot_checkpoint:
raise ValueError("resume and snapshot checkpoint paths must differ")
parent = os.path.dirname(snapshot_checkpoint)
_require_real_directory(parent, "snapshot checkpoint parent")
source_manifest = _checkpoint_manifest(
resume_checkpoint,
"resume checkpoint",
)
if (
source_manifest["metadata"]["step"] != expected_step
or source_manifest["metadata"] != expected_metadata
):
raise ValueError("resume checkpoint metadata differs from loaded state")
if os.path.lexists(snapshot_checkpoint):
snapshot_manifest = _checkpoint_manifest(
snapshot_checkpoint,
"immortal snapshot checkpoint",
)
if snapshot_manifest != source_manifest:
raise ValueError(
"refusing to replace a differing immortal snapshot checkpoint"
)
_fsync_directory(parent)
return False
staging = _create_snapshot_recovery_staging(snapshot_checkpoint)
published = False
try:
for name in CHECKPOINT_FILENAMES:
_copy_regular_file(
os.path.join(resume_checkpoint, name),
os.path.join(staging, name),
f"snapshot recovery {name}",
)
fsync_checkpoint_tree(staging)
if _checkpoint_manifest(staging, "staged snapshot checkpoint") != (
source_manifest
):
raise ValueError("staged snapshot differs from resume checkpoint")
if _checkpoint_manifest(
resume_checkpoint,
"resume checkpoint after snapshot copy",
) != source_manifest:
raise ValueError("resume checkpoint changed during snapshot copy")
try:
_rename_directory_no_replace(staging, snapshot_checkpoint)
published = True
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
snapshot_manifest = _checkpoint_manifest(
snapshot_checkpoint,
"published immortal snapshot checkpoint",
)
if snapshot_manifest != source_manifest:
raise ValueError(
"published immortal snapshot differs from resume checkpoint"
)
_fsync_directory(parent)
return published
finally:
if os.path.lexists(staging):
_remove_snapshot_recovery_staging(staging)
def _active_snapshot_marker_count(raw_log, step, resume_log_path):
marker = json.dumps({"step": step, "snapshot": True}).encode("ascii")
resume_prefix = f"resumed from {resume_log_path} at step ".encode("ascii")
active_count = 0
for line in raw_log.splitlines():
if line.startswith(resume_prefix):
raw_step = line[len(resume_prefix):]
if re.fullmatch(rb"0|[1-9][0-9]*", raw_step):
resume_step = int(raw_step)
if resume_step < step:
active_count = 0
elif line == marker:
active_count += 1
return active_count
def prepare_snapshot_boundary_recovery(
resume_checkpoint,
snapshot_checkpoint,
train_log_path,
resume_log_path,
expected_step,
expected_metadata,
):
"""
Verify/recreate a snapshot and return its exact missing stdout marker.
Resume lines below the snapshot step abandon all earlier markers at that
step. At most one marker may remain active; repeated recovery at the same
boundary therefore emits nothing.
"""
ensure_snapshot_checkpoint(
resume_checkpoint,
snapshot_checkpoint,
expected_step,
expected_metadata,
)
if os.path.lexists(train_log_path):
result = _stable_regular_file(
train_log_path,
"training log used for snapshot recovery",
capture_bytes=True,
)
raw_log = result["bytes"]
else:
raw_log = b""
active_count = _active_snapshot_marker_count(
raw_log,
expected_step,
resume_log_path,
)
if active_count > 1:
raise ValueError(
"training log has duplicate active snapshot markers at resume step"
)
if active_count == 1:
return None
return json.dumps(
{"step": expected_step, "snapshot": True}
).encode("ascii")
def fsync_checkpoint_tree(path):
"""Flush every staged file and its directory before publication."""
_require_real_directory(path, "staged checkpoint")
for name in sorted(os.listdir(path)):
item = os.path.join(path, name)
if os.path.islink(item) or not os.path.isfile(item):
raise ValueError(
f"staged checkpoint contains a non-file entry: {name}"
)
descriptor = os.open(item, os.O_RDONLY)
try:
os.fsync(descriptor)
finally:
os.close(descriptor)
_fsync_directory(path)
def _swap_directories_macos(left, right):
if sys.platform != "darwin":
raise RuntimeError(
"atomic checkpoint directory exchange requires macOS"
)
libc = ctypes.CDLL(None, use_errno=True)
renamex_np = libc.renamex_np
renamex_np.argtypes = [
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.c_uint,
]
renamex_np.restype = ctypes.c_int
result = renamex_np(
os.fsencode(left),
os.fsencode(right),
RENAME_SWAP,
)
if result != 0:
error = ctypes.get_errno()
raise OSError(
error,
os.strerror(error),
f"{left} <-> {right}",
)
def install_checkpoint(staging, target):
"""Publish a complete staged checkpoint without removing the live path."""
staging = os.path.abspath(staging)
target = os.path.abspath(target)
parent = os.path.dirname(target)
previous = target + ".prev"
_require_real_directory(staging, "staged checkpoint")
fsync_checkpoint_tree(staging)
if os.path.lexists(target):
_require_real_directory(target, "current checkpoint")
if os.path.lexists(previous):
if os.path.islink(previous) or not os.path.isdir(previous):
raise ValueError(
f"previous checkpoint is not a real directory: {previous}"
)
shutil.rmtree(previous)
_swap_directories_macos(staging, target)
os.rename(staging, previous)
else:
os.rename(staging, target)
_fsync_directory(parent)
return target
def recover_checkpoint(target):
"""Recover the last canonical checkpoint from a legacy rename gap."""
target = os.path.abspath(target)
if os.path.lexists(target):
_require_real_directory(target, "current checkpoint")
return False
previous = target + ".prev"
if not os.path.lexists(previous):
return False
_require_real_directory(previous, "previous checkpoint")
try:
os.rename(previous, target)
except OSError as exc:
if exc.errno == errno.ENOENT and os.path.isdir(target):
return False
raise
_fsync_directory(os.path.dirname(target))
return True
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