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: 35,231 Bytes
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import json
import os
import sys
import tempfile
from types import SimpleNamespace
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from scripts.hf_metadata import ( # noqa: E402
ADDITIONAL_SPECIAL_TOKENS,
_interval,
_metric,
_verdict,
development_evaluation_section,
file_sha256,
generation_config,
llama_config,
mtp_config,
require_unchanged_files,
render_evaluation_section,
render_model_card,
special_tokens_map,
tokenizer_config,
validate_external_verification_receipt,
validate_export_checkpoint,
verify_export_manifest,
write_json_atomic,
write_export_manifest,
)
def main():
args = SimpleNamespace(
vocab_size=32768,
dim=768,
n_layers=12,
n_heads=12,
n_kv_heads=4,
head_dim=64,
ffn_hidden=2048,
max_seq_len=2048,
rope_theta=100000.0,
norm_eps=1e-5,
tie_embeddings=True,
mtp_layers=1,
mtp_depth=2,
)
config = llama_config(args)
assert config["architectures"] == ["LlamaForCausalLM"]
assert config["vocab_size"] == 32768
assert config["eos_token_id"] == 0
assert config["pad_token_id"] == 1
assert config["bos_token_id"] is None
tokenizer = tokenizer_config(args)
assert tokenizer["tokenizer_class"] == "PreTrainedTokenizerFast"
assert tokenizer["model_max_length"] == args.max_seq_len
assert tokenizer["eos_token"] == "<|endoftext|>"
assert tokenizer["pad_token"] == "<|pad|>"
assert tokenizer["additional_special_tokens"] == ADDITIONAL_SPECIAL_TOKENS
special = special_tokens_map()
assert special["additional_special_tokens"] == ADDITIONAL_SPECIAL_TOKENS
generation = generation_config()
assert generation["eos_token_id"] == config["eos_token_id"]
assert generation["pad_token_id"] == config["pad_token_id"]
mtp = mtp_config(args, {"step": 123})
assert mtp["trained_steps"] == 123
assert mtp["recursive"] is True
assert "does not guarantee close distributions" in mtp["note"]
validation_report = {
"publication_ready": True,
"summary": {
"main_loss": {"mean": 4.2, "ci95": [4.1, 4.3]},
"main_perplexity": 66.69,
"mtp_loss": [
{"mean": 4.4, "ci95": [4.3, 4.5]},
{"mean": 4.6, "ci95": [4.5, 4.7]},
],
},
}
acceptance_report = {
"schema_version": 1,
"documents": 200,
"trained_primary_endpoint": {
"ratio": 1.1,
"ci95": [1.02, 1.18],
"verdict": "POSITIVE",
},
"trained_minus_untrained_ratio": {
"difference": 0.08,
"ci95": [0.01, 0.15],
"verdict": "CLEARS_CONTROL",
},
"combined_interpretation": "POSITIVE_AND_CLEARS_CONTROL",
}
format_ablation_report = {
"schema_version": 1,
"publication_ready": True,
"documents": 200,
"baseline_revision_evidence": {
"limitation": (
"Repository revisions were captured after the run 1 corpus "
"build, so source-stream drift cannot be ruled out."
),
},
"runtime_code_evidence": {
"limitation": (
"Run 1 source hashes were not recorded, so exact source-state "
"equivalence cannot be proven from its checkpoint."
),
},
"primary_endpoint": {
"difference_in_differences": 0.06,
"ci95": [0.01, 0.11],
"verdict": "POSITIVE",
},
"secondary_endpoint": {
"difference_in_differences": 0.03,
"ci95": [-0.01, 0.07],
"verdict": "NULL: the interval includes zero",
},
}
rollout_report = {
"schema_version": 2,
"instrument_version": 3,
"publication_ready": True,
"quality_gate": {
"reference": "branch_local_full_sequence_replay",
"rule": (
"every_emitted_token_equals_reference_argmax_or_is_a_"
"certified_bf16_near_tie_on_the_same_realized_prefix"
),
"near_tie_max_ulps": 8,
"scored_policy_documents": 600,
"scored_tokens": 38400,
"exact_argmax_tokens": 38399,
"certified_near_tie_tokens": 1,
"failed_tokens": 0,
"branch_replay_passes": 600,
"cross_policy_trajectory_matches": 100,
"passed": True,
},
"primary_endpoint": {
"comparison": "selected_adaptive_minus_selected_fixed",
"metric": "accepted_drafts_per_verification",
"difference": 0.2,
"ci95": [0.1, 0.3],
"verdict": "POSITIVE",
"fixed_policy": "fixed_d2",
"adaptive_policy": "adaptive_h0.5",
"documents": 60,
},
"secondary_target_forward_endpoint": {
"comparison": "selected_adaptive_minus_selected_fixed",
"metric": "output_tokens_per_target_forward",
"adaptive_policy": "adaptive_h0.5",
"fixed_policy": "fixed_d2",
"difference": 0.04,
"ci95": [0.01, 0.07],
"documents": 60,
"verdict": "POSITIVE",
},
"secondary_draft_issued_proxy_endpoint": {
"comparison": "selected_adaptive_minus_selected_fixed",
"metric": "drafts_issued_per_output_token",
"adaptive_policy": "adaptive_h0.5",
"fixed_policy": "fixed_d2",
"difference": -0.03,
"ci95": [-0.05, -0.01],
"documents": 60,
"verdict": "NEGATIVE",
},
"secondary_draft_work_endpoint": {
"comparison": "selected_adaptive_minus_selected_fixed",
"metric": "draft_recursions_per_output_token",
"adaptive_policy": "adaptive_h0.5",
"fixed_policy": "fixed_d2",
"difference": -0.02,
"ci95": [-0.04, 0.00],
"documents": 60,
"verdict": "NULL: the interval includes 0",
},
"endpoint_scope_note": (
"The primary endpoint measures accepted drafts per verification. "
"It does not establish verification-width cost or deployment "
"latency. Draft recursions per output token is the registered "
"drafter-work companion; issued drafts per output token is retained "
"only as an issuance proxy. Target forwards exclude the added "
"post-hoc branch-replay forward and independent verification pass."
),
}
evaluation = render_evaluation_section(
validation_report,
acceptance_report,
format_ablation_report,
rollout_report,
)
assert "Final validation main NLL | 4.2000" in evaluation
assert "POSITIVE_AND_CLEARS_CONTROL" in evaluation
assert "FIM-training effect" in evaluation
assert "source-stream drift cannot be ruled out" in evaluation
assert "exact source-state equivalence cannot be proven" in evaluation
assert "adaptive_h0.5 versus fixed_d2" in evaluation
assert (
"Exact argmax on 38399 of 38400 emitted tokens; "
"1 token certified as a bfloat16 near-tie within 8 ulps"
) in evaluation
assert "Branch-local greedy replay" in evaluation
assert "Cross-policy output identity" in evaluation
assert "drafts issued per output token" in evaluation
assert "draft recursions per output token" in evaluation
assert "issuance proxy" in evaluation
assert "verification-width cost" in evaluation
assert "deployment latency" in evaluation
assert "post-hoc branch-replay forward" in evaluation
assert "not a signed external or trusted-execution witness" in evaluation
assert "Greedy-output equivalence" not in evaluation
all_exact = render_evaluation_section(
validation_report,
acceptance_report,
format_ablation_report,
{
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"exact_argmax_tokens": 38400,
"certified_near_tie_tokens": 0,
},
},
)
assert "Exact argmax for all 38400 emitted tokens" in all_exact
def _expect_evaluation_reject(
bad_validation=None,
bad_acceptance=None,
bad_format_ablation=None,
bad_rollout=None,
expected_substring=None,
):
try:
render_evaluation_section(
bad_validation if bad_validation is not None else validation_report,
bad_acceptance if bad_acceptance is not None else acceptance_report,
bad_format_ablation
if bad_format_ablation is not None
else format_ablation_report,
bad_rollout if bad_rollout is not None else rollout_report,
)
except ValueError as exc:
assert expected_substring in str(exc), (
f"expected {expected_substring!r} in {exc}"
)
else:
raise AssertionError(
f"an evaluation section that should have failed on "
f"{expected_substring!r} was accepted"
)
_expect_evaluation_reject(
bad_validation={**validation_report, "publication_ready": False},
expected_substring="validation report is not publication-ready",
)
_expect_evaluation_reject(
bad_rollout={**rollout_report, "publication_ready": False},
expected_substring="rollout report is not publication-ready",
)
_expect_evaluation_reject(
bad_acceptance={**acceptance_report, "schema_version": 2},
expected_substring="acceptance comparison schema is not 1",
)
_expect_evaluation_reject(
bad_format_ablation={**format_ablation_report, "schema_version": 2},
expected_substring="format-ablation comparison schema is not 1",
)
_expect_evaluation_reject(
bad_format_ablation={
**format_ablation_report,
"publication_ready": False,
},
expected_substring="format-ablation comparison is not publication-ready",
)
_expect_evaluation_reject(
bad_validation={
**validation_report,
"summary": {
**validation_report["summary"],
"mtp_loss": [{"mean": 4.4, "ci95": [4.3, 4.5]}],
},
},
expected_substring="does not have two MTP losses",
)
_expect_evaluation_reject(
bad_acceptance={**acceptance_report, "documents": 1},
expected_substring="acceptance document count is invalid",
)
_expect_evaluation_reject(
bad_format_ablation={**format_ablation_report, "documents": 1},
expected_substring="format-ablation document count is invalid",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"passed": False,
},
},
expected_substring="rollout branch-local replay did not pass",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"certified_near_tie_tokens": -1,
},
},
expected_substring="invalid certified near tie tokens count",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"schema_version": 1,
"instrument_version": 2,
"quality_gate": {
"reference": "greedy_ar",
"rule": "exact_token_match_or_certified_near_tie",
"near_tie_max_ulps": 8,
"exact_ar_matches": 510,
"certified_divergences": 90,
"passed": True,
},
},
expected_substring="requires rollout schema 2 instrument 3",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"exact_argmax_tokens": 38398,
},
},
expected_substring="rollout branch-local replay did not pass",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"branch_replay_passes": 599,
},
},
expected_substring="rollout branch-local replay did not pass",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"quality_gate": {
**rollout_report["quality_gate"],
"cross_policy_trajectory_matches": 99,
},
},
expected_substring="rollout cross-policy output identity did not pass",
)
_expect_evaluation_reject(
bad_rollout={
**rollout_report,
"primary_endpoint": {
**rollout_report["primary_endpoint"],
"documents": 1,
},
},
expected_substring="rollout test document count is invalid",
)
try:
_metric(float("nan"), "a metric")
except ValueError as exc:
assert "is not finite" in str(exc)
else:
raise AssertionError("a NaN metric value was accepted")
try:
_interval([1.0], "an interval")
except ValueError as exc:
assert "is not a two-element interval" in str(exc)
else:
raise AssertionError("a one-element interval was accepted")
try:
_interval([2.0, 1.0], "an interval")
except ValueError as exc:
assert "is reversed" in str(exc)
else:
raise AssertionError("a reversed interval was accepted")
try:
_verdict("line one\nline two", "a verdict")
except ValueError as exc:
assert "not a safe single-line verdict" in str(exc)
else:
raise AssertionError("a multi-line verdict was accepted")
try:
_verdict("has | a pipe", "a verdict")
except ValueError as exc:
assert "not a safe single-line verdict" in str(exc)
else:
raise AssertionError("a verdict containing a table pipe was accepted")
development = development_evaluation_section(100, 123)
assert "step 100 of 123" in development
assert "no final registered evaluation claims" in development
with tempfile.NamedTemporaryFile("w", encoding="utf-8") as f:
f.write("load {{REPO_ID}} here\n{{FINAL_EVALUATION}}\n")
f.flush()
rendered = render_model_card(f.name, "owner/model", evaluation)
assert rendered.startswith("load owner/model here\n")
assert "Final validation main NLL" in rendered
assert "{{FINAL_EVALUATION}}" not in rendered
try:
render_model_card(f.name, "missing-slash", evaluation)
except ValueError:
pass
else:
raise AssertionError("invalid repository ID was accepted")
with tempfile.NamedTemporaryFile("w", encoding="utf-8") as f:
f.write("no repo id placeholder here\n{{FINAL_EVALUATION}}\n")
f.flush()
try:
render_model_card(f.name, "owner/model", evaluation)
except ValueError as exc:
assert "must contain the placeholder {{REPO_ID}}" in str(exc)
else:
raise AssertionError(
"a template missing the repo-id placeholder was accepted"
)
with tempfile.NamedTemporaryFile("w", encoding="utf-8") as f:
f.write("{{REPO_ID}}\nno evaluation placeholder here\n")
f.flush()
try:
render_model_card(f.name, "owner/model", evaluation)
except ValueError as exc:
assert "must contain the placeholder {{FINAL_EVALUATION}}" in str(exc)
else:
raise AssertionError(
"a template missing the evaluation placeholder was accepted"
)
with tempfile.NamedTemporaryFile("w", encoding="utf-8") as f:
f.write("{{REPO_ID}}\n{{FINAL_EVALUATION}}\n")
f.flush()
try:
render_model_card(f.name, "owner/model", " ")
except ValueError as exc:
assert "evaluation text is empty" in str(exc)
else:
raise AssertionError("blank evaluation text was accepted")
# A leaked literal placeholder inside the evaluation text survives
# rendering unresolved: REPO_ID substitution runs on the raw template
# before the evaluation text is spliced in, so a placeholder-looking
# string arriving *inside* evaluation_markdown is never touched.
leaking_evaluation = "See {{REPO_ID}} for details."
try:
render_model_card(f.name, "owner/model", leaking_evaluation)
except ValueError as exc:
assert "unresolved placeholder" in str(exc)
else:
raise AssertionError(
"a literal placeholder leaking through evaluation text was "
"accepted"
)
with tempfile.NamedTemporaryFile("w+b") as f:
f.write(b"frozen")
f.flush()
evidence = {
"holdout": {
"path": f.name,
"sha256": file_sha256(f.name),
}
}
require_unchanged_files(evidence, "test evaluation")
f.write(b"mutation")
f.flush()
try:
require_unchanged_files(evidence, "test evaluation")
except RuntimeError as exc:
assert "holdout changed during test evaluation" in str(exc)
else:
raise AssertionError("an input mutation passed snapshot validation")
final_meta = {
"step": 123,
"config": {
"max_steps": 123,
"run_name": "wisp-test",
},
"model_args": {"dim": 768},
"optimizer_state_included": True,
}
assert validate_export_checkpoint(final_meta)["complete"] is True
unattested_final = {
**final_meta,
"optimizer_state_included": False,
}
try:
validate_export_checkpoint(unattested_final)
except ValueError as exc:
assert "optimizer state was saved" in str(exc)
else:
raise AssertionError("an unattested final checkpoint was releasable")
snapshot_meta = {
**final_meta,
"step": 100,
}
try:
validate_export_checkpoint(snapshot_meta)
except ValueError as exc:
assert "not final step" in str(exc)
else:
raise AssertionError("an incomplete checkpoint was releasable by default")
assert validate_export_checkpoint(
snapshot_meta, allow_incomplete=True
)["complete"] is False
control_meta = {
**final_meta,
"config": {
**final_meta["config"],
"initialization_only": True,
},
}
try:
validate_export_checkpoint(control_meta)
except ValueError as exc:
assert "initialization-only control" in str(exc)
else:
raise AssertionError("an untrained control was releasable")
required = [
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"mtp.safetensors",
"mtp_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
]
with tempfile.TemporaryDirectory() as out_dir:
for name in required:
with open(os.path.join(out_dir, name), "wb") as f:
content = (
b"owner/model" if name == "README.md" else name.encode()
)
f.write(content)
write_export_manifest(
out_dir,
final_meta,
100,
20,
"owner/model",
{
"meta_sha256": "a" * 64,
"master_sha256": "b" * 64,
"optimizer_sha256": "c" * 64,
},
True,
{
"validation": {"sha256": "d" * 64},
"acceptance_comparison": {"sha256": "e" * 64},
"format_ablation": {"sha256": "1" * 64},
"rollout": {"sha256": "f" * 64},
},
"0" * 64,
)
with open(os.path.join(out_dir, "export_manifest.json")) as f:
manifest = json.load(f)
assert manifest["schema_version"] == 3
assert manifest["repo_id"] == "owner/model"
assert manifest["release_complete"] is True
assert manifest["evaluation_sources"]["validation"]["sha256"] == (
"d" * 64
)
assert manifest["model_card_template_sha256"] == "0" * 64
assert manifest["source_checkpoint"]["step"] == 123
assert manifest["source_checkpoint"]["meta_sha256"] == "a" * 64
assert manifest["source_checkpoint"]["master_sha256"] == "b" * 64
assert manifest["source_checkpoint"]["optimizer_sha256"] == "c" * 64
assert manifest["trunk_parameters"] == 100
assert manifest["mtp_parameters_excluding_shared_embedding_and_head"] == 20
assert sorted(manifest["files"]) == required
assert all(len(item["sha256"]) == 64 for item in manifest["files"].values())
verify_export_manifest(out_dir)
manifest_path = os.path.join(out_dir, "export_manifest.json")
invalid_manifest = json.loads(json.dumps(manifest))
invalid_manifest["evaluation_sources"] = None
with open(manifest_path, "w", encoding="utf-8") as f:
json.dump(invalid_manifest, f, indent=2, sort_keys=True)
f.write("\n")
try:
verify_export_manifest(out_dir)
except ValueError as exc:
assert "evaluation sources" in str(exc)
else:
raise AssertionError(
"a final export without evaluation sources passed verification"
)
with open(manifest_path, "w", encoding="utf-8") as f:
json.dump(manifest, f, indent=2, sort_keys=True)
f.write("\n")
manifest_sha256 = file_sha256(
manifest_path
)
receipt = {
"schema_version": 1,
"created_at": "2026-07-25T23:00:00+00:00",
"status": "verified",
"passed": True,
"package": {
"export_dir": os.path.abspath(out_dir),
"repo_id": "owner/model",
"export_manifest_sha256": manifest_sha256,
"source_checkpoint": manifest["source_checkpoint"],
},
"checkpoint": {
"path": os.path.abspath("/tmp/checkpoint"),
"meta_sha256": "a" * 64,
"master_sha256": "b" * 64,
"optimizer_sha256": "c" * 64,
},
"probe": {
"seed": 0,
"length": 24,
"token_ids_sha256": "d" * 64,
},
"tokenizer": {
"special_token_ids": list(range(7)),
"eos_token_id": 0,
"pad_token_id": 1,
"byte_roundtrip_exact": True,
"probe_sha256": "e" * 64,
},
"logits": {
"shape": [24, 32768],
"max_abs_delta": 0.02,
"logit_scale": 10.0,
"relative_max_abs_delta": 0.002,
"relative_delta_threshold": 0.01,
"argmax_agreement": 1.0,
},
"toolchain": {
"python": "3.12.0",
"platform": "macOS-26-arm64",
"mlx": "0.29.0",
"torch": "2.9.0",
"transformers": "4.57.0",
},
}
validate_external_verification_receipt(
receipt, manifest, manifest_sha256
)
receipt_path = os.path.join(out_dir, "external-receipt.json")
write_json_atomic(receipt_path, receipt)
try:
write_json_atomic(receipt_path, receipt)
except FileExistsError as exc:
assert "refusing to replace JSON artifact" in str(exc)
else:
raise AssertionError("an external receipt was silently overwritten")
# The TOCTOU guard (path appears between staging and rename) is
# distinct from the refuse-to-overwrite guard just exercised above --
# neutralizing either one alone still raises FileExistsError from the
# other, so a bare `except FileExistsError` can't tell them apart.
# Simulated by making os.path.lexists lie: absent on the first check,
# present on the second, since there's no real concurrency here.
real_lexists = os.path.lexists
call_count = [0]
racing_path = os.path.join(out_dir, "racing.json")
def racing_lexists(path):
call_count[0] += 1
if call_count[0] == 1:
return False
if path == os.path.abspath(racing_path):
return True
return real_lexists(path)
try:
os.path.lexists = racing_lexists
try:
write_json_atomic(racing_path, {"k": "v"})
except FileExistsError as exc:
assert "JSON target appeared during staging" in str(exc)
else:
raise AssertionError(
"a JSON target appearing mid-staging was not caught"
)
finally:
os.path.lexists = real_lexists
assert not real_lexists(racing_path), (
"the racing rename should never have actually happened"
)
os.unlink(receipt_path)
mutated_receipt = json.loads(json.dumps(receipt))
mutated_receipt["checkpoint"]["master_sha256"] = "f" * 64
try:
validate_external_verification_receipt(
mutated_receipt, manifest, manifest_sha256
)
except ValueError as exc:
assert "checkpoint hashes" in str(exc)
else:
raise AssertionError("a receipt for different weights was accepted")
mutated_receipt = json.loads(json.dumps(receipt))
mutated_receipt["logits"]["argmax_agreement"] = 0.99
try:
validate_external_verification_receipt(
mutated_receipt, manifest, manifest_sha256
)
except ValueError as exc:
assert "argmax agreement" in str(exc)
else:
raise AssertionError("a nonexact argmax comparison was accepted")
mutated_receipt = json.loads(json.dumps(receipt))
mutated_receipt["logits"]["relative_max_abs_delta"] = -1.0
try:
validate_external_verification_receipt(
mutated_receipt, manifest, manifest_sha256
)
except ValueError as exc:
assert "relative logit delta" in str(exc)
else:
raise AssertionError("a negative relative delta was accepted")
config_path = os.path.join(out_dir, "config.json")
with open(config_path, "ab") as f:
f.write(b"mutation")
try:
verify_export_manifest(out_dir)
except ValueError as exc:
assert "does not match" in str(exc)
else:
raise AssertionError("a mutated export payload passed verification")
with tempfile.TemporaryDirectory() as out_dir:
for name in required:
with open(os.path.join(out_dir, name), "wb") as f:
content = (
b"owner/model" if name == "README.md" else name.encode()
)
f.write(content)
write_export_manifest(
out_dir,
final_meta,
100,
20,
"owner/model",
{
"meta_sha256": "a" * 64,
"master_sha256": "b" * 64,
"optimizer_sha256": "c" * 64,
},
True,
{
"validation": {"sha256": "d" * 64},
"acceptance_comparison": {"sha256": "e" * 64},
"format_ablation": {"sha256": "1" * 64},
"rollout": {"sha256": "f" * 64},
},
"0" * 64,
)
os.makedirs(os.path.join(out_dir, "unexpected"))
try:
verify_export_manifest(out_dir)
except ValueError as exc:
assert "unexpected or missing" in str(exc)
else:
raise AssertionError("an extra export directory passed verification")
with tempfile.TemporaryDirectory() as out_dir:
for name in required[:-1]:
with open(os.path.join(out_dir, name), "wb") as f:
f.write(name.encode())
try:
write_export_manifest(
out_dir,
final_meta,
100,
20,
"owner/model",
{
"meta_sha256": "a" * 64,
"master_sha256": "b" * 64,
"optimizer_sha256": "c" * 64,
},
True,
{
"validation": {"sha256": "d" * 64},
"acceptance_comparison": {"sha256": "e" * 64},
"format_ablation": {"sha256": "1" * 64},
"rollout": {"sha256": "f" * 64},
},
"0" * 64,
)
except FileNotFoundError as exc:
assert "release artifact is missing" in str(exc)
else:
raise AssertionError(
"an export directory missing a required file was accepted"
)
def build_manifest_dict():
return {
"schema_version": 3,
"repo_id": "owner/model",
"release_complete": True,
"evaluation_sources": {
"validation": {"sha256": "d" * 64},
"acceptance_comparison": {"sha256": "e" * 64},
"format_ablation": {"sha256": "1" * 64},
"rollout": {"sha256": "f" * 64},
},
"model_card_template_sha256": "0" * 64,
"source_checkpoint": {
"step": 123,
"meta_sha256": "a" * 64,
"master_sha256": "b" * 64,
"optimizer_sha256": "c" * 64,
},
"trunk_parameters": 100,
"mtp_parameters_excluding_shared_embedding_and_head": 20,
"files": {},
}
def build_export_dir(mutate=None):
out_dir = tempfile.mkdtemp()
for name in required:
path = os.path.join(out_dir, name)
content = b"owner/model" if name == "README.md" else name.encode()
with open(path, "wb") as f:
f.write(content)
manifest = build_manifest_dict()
manifest["files"] = {
name: {
"bytes": os.path.getsize(os.path.join(out_dir, name)),
"sha256": file_sha256(os.path.join(out_dir, name)),
}
for name in required
}
if mutate is not None:
mutate(manifest)
with open(
os.path.join(out_dir, "export_manifest.json"), "w", encoding="utf-8"
) as f:
json.dump(manifest, f, indent=2, sort_keys=True)
f.write("\n")
return out_dir
def _expect_manifest_reject(mutate, expected_substring):
out_dir = build_export_dir(mutate)
try:
verify_export_manifest(out_dir)
except ValueError as exc:
assert expected_substring in str(exc), (
f"expected {expected_substring!r} in {exc}"
)
else:
raise AssertionError(
f"a manifest that should have failed on "
f"{expected_substring!r} passed verification"
)
_expect_manifest_reject(
lambda m: m.__setitem__("schema_version", 2),
"must use schema_version 3",
)
_expect_manifest_reject(
lambda m: m.__setitem__("release_complete", "yes"),
"release_complete is not boolean",
)
_expect_manifest_reject(
lambda m: m.__setitem__("model_card_template_sha256", "not-hex"),
"model card template hash is not a SHA-256",
)
_expect_manifest_reject(
lambda m: (
m.__setitem__("release_complete", False),
m.__setitem__(
"evaluation_sources", {"validation": {"sha256": "d" * 64}}
),
),
"development export must not declare final evaluation sources",
)
_expect_manifest_reject(
lambda m: m.__setitem__("files", "not-a-dict"),
"export manifest has no files object",
)
_expect_manifest_reject(
lambda m: m["files"].pop("LICENSE"),
"export manifest payload differs from required files",
)
_expect_manifest_reject(
lambda m: m.__setitem__("source_checkpoint", "not-a-dict"),
"export manifest has no source_checkpoint",
)
_expect_manifest_reject(
lambda m: m["source_checkpoint"].__setitem__("step", -1),
"source_checkpoint.step is not a positive integer",
)
_expect_manifest_reject(
lambda m: m["source_checkpoint"].__setitem__("meta_sha256", "not-hex"),
"source_checkpoint.meta_sha256 is not a SHA-256",
)
# README.md must actually contain the manifest's repo_id string. The
# README's hash has to match what the manifest recorded (a hash mismatch
# would trip the "does not match export manifest" check first) but its
# *content* must lack the repo_id, so build a directory whose README
# never contained it in the first place.
out_dir = tempfile.mkdtemp()
for name in required:
path = os.path.join(out_dir, name)
content = (
b"a different repo entirely, no repo id here"
if name == "README.md"
else name.encode()
)
with open(path, "wb") as f:
f.write(content)
manifest = build_manifest_dict()
manifest["files"] = {
name: {
"bytes": os.path.getsize(os.path.join(out_dir, name)),
"sha256": file_sha256(os.path.join(out_dir, name)),
}
for name in required
}
with open(
os.path.join(out_dir, "export_manifest.json"), "w", encoding="utf-8"
) as f:
json.dump(manifest, f, indent=2, sort_keys=True)
f.write("\n")
try:
verify_export_manifest(out_dir)
except ValueError as exc:
assert "does not match export manifest repo_id" in str(exc)
else:
raise AssertionError(
"a rendered model card missing the repo id was accepted"
)
print("Hugging Face export metadata: PASS")
if __name__ == "__main__":
main()
|