mlx-genkit

Small, reusable MLX generation and training toolkit that brings HF/Torch generate() feature parity and clean persona steering to Apple Silicon. It reuses mlx-lm primitives (caches, projections, speculative) and fills the missing parity pieces.

Features

• HF-style GenerationConfig with processors/warpers: repetition penalty, no-repeat-ngrams, frequency/presence, bad-words, min_new_tokens, typical_p, epsilon_cutoff.
• Constraints: force_words_ids (strict start + continuation), suppress_tokens, begin_suppress_tokens, multiple eos_token_ids, forced BOS/EOS and per-position forced_decoder_ids.
• Modes: sampling (fast path via mlx-lm), beam (num_beams, length_penalty, early_stopping), speculative (mlx-lm), sliding KV (max_kv_size).
• Hooks: ResidualInjectionHook (sampling) and LogitBiasHook (sampling/beam); SoftPromptHook for training.
• Structured output enforcement: JSON-schema adherence, retry policies, semantic checks, and grammar stubs.
• Streaming with incremental validation: token callbacks, schema-aware early exits, and detailed result metadata.
• Batch helpers, JSONL adherence logging, and an eval harness for prompt suites.
• Training (MLX): loss_forward, xent_loss (label smoothing), mixed-precision compute (bf16) with fp32 master weights.
• Training utilities: sequence_logprob, token_kl for scoring and policy KL.
• Model helpers: ema_update, build_action_mask, stable_softmax; best-effort clone_reference.

Install

• From PyPI (recommended):

pip install mlx-genkit

• Dependencies (if not already installed):

pip install mlx mlx-lm transformers

• From source (editable):

pip install -e .

Models from Hugging Face

• If the repo provides MLX weights (e.g., in mlx-community), you can load directly: load('mlx-community/<model>').
• For standard HF (PyTorch) repos, convert once using mlx-lm:
  • Python: from mlx_genkit.interop import convert_hf_to_mlx; convert_hf_to_mlx('Qwen/Qwen3-0.6B', quantize=False, local_out='mlx_qwen3_0_6b')
  • CLI: mlx_lm.convert --hf-path Qwen/Qwen3-0.6B --mlx-path mlx_qwen3_0_6b
  • Then load with load('mlx_qwen3_0_6b').

Auto-convert loader

• You can pass either an HF repo id or a local MLX path to auto_load, which will convert once and cache under ./mlx_cache/<sanitized_repo_id>:

from mlx_genkit.loader import auto_load
model, tokenizer, local_path = auto_load('Qwen/Qwen3-0.6B')
print('Loaded from', local_path)  # e.g., ./mlx_cache/Qwen_Qwen3-0.6B

Basic usage

from mlx_genkit import GenerationConfig, generate
from mlx_lm import load

model, tokenizer = load('mlx_qwen3_0_6b')
cfg = GenerationConfig(max_tokens=64, temperature=0.7, top_p=0.95, seed=17)
out = generate(model, tokenizer, 'Hello MLX parity', cfg)
print(out['text'])

Chat prompts (auto chat template)

# If you pass a list of HF-style messages, mlx-genkit will automatically
# apply the tokenizer's chat template when available.
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Summarize MLX in 3 bullets."},
]
cfg = GenerationConfig(max_tokens=64, temperature=0.7)
out = generate(model, tokenizer, messages, cfg)
print(out['text'])

Auto-apply chat template for plain prompts

# You can also provide a plain string and have mlx-genkit wrap it
# using the model's chat template when available. This is enabled
# automatically if the tokenizer defines a chat_template; you can
# force or disable it via GenerationConfig, or force with assume_user_chat.
cfg = GenerationConfig(max_tokens=64, temperature=0.7, auto_chat_template=True, system_prompt="You are helpful")
# Equivalent explicit flag:
# cfg = GenerationConfig(max_tokens=64, temperature=0.7, assume_user_chat=True, system_prompt="You are helpful")
out = generate(model, tokenizer, "Summarize MLX in 3 bullets.", cfg)
print(out['text'])

Beam and constraints

cfg = GenerationConfig(max_tokens=64, temperature=0.0, num_beams=4, early_stopping=True, length_penalty=0.2,
                       force_words_ids=[tokenizer.encode(' cat')], min_new_tokens=8,
                       bad_words_ids=[[tokenizer.eos_token_id]], suppress_tokens=[tokenizer.eos_token_id])
out = generate(model, tokenizer, 'The', cfg)

Speculative decoding

cfg = GenerationConfig(max_tokens=64, temperature=0.7, top_p=0.95,
                       use_speculative=True, draft_model_id='mlx_qwen3_0_6b', num_draft_tokens=3)
out = generate(model, tokenizer, 'Speculative test', cfg)

Structured JSON output

from mlx_genkit import GenerationConfig, JsonAdherence, generate

schema = {
    "type": "object",
    "properties": {
        "summary": {"type": "string"},
        "confidence": {"type": "number"}
    },
    "required": ["summary", "confidence"],
}

cfg = GenerationConfig(max_tokens=220, temperature=0.0)
result = generate(
    model,
    tokenizer,
    "Summarise the change log in JSON",
    cfg,
    json_schema=schema,
    adherence=JsonAdherence(retries=2, strict_only_json=True),
)
print(result.json)

• Structured adherence automatically strips common ```json fenced output and keeps retrying with escalating prompts when JsonAdherence(retries=...) is configured. Use `strict_only_json=True` to reject any non-JSON commentary.

Structured DSL

from mlx_genkit import StructuredSpec, generate_structured

spec = StructuredSpec(
    schema=schema,
    fields=["summary", "confidence"],
    examples=[{"input": "Bug fix", "output": {"summary": "...", "confidence": 0.9}}],
)
res = generate_structured(model, tokenizer, task="Summarise the diff", spec=spec)
print(res.json)

Streaming & incremental validation

from mlx_genkit import (
    GenerationConfig,
    JsonAdherence,
    StreamCallbacks,
    generate_stream,
)

def stream_json(model, tokenizer, prompt, schema):
    emitted = []

    def on_token(token, idx):
        emitted.append(token)
        piece = tokenizer.decode([token]) if isinstance(token, int) else str(token)
        if piece:
            print(piece, end="", flush=True)

    def on_invalid_path(info):
        print("\n[invalid path detected]", info["message"])

    callbacks = StreamCallbacks(on_token=on_token, on_invalid_path=on_invalid_path)
    result = generate_stream(
        model,
        tokenizer,
        prompt,
        GenerationConfig(max_tokens=128, temperature=0.0),
        json_schema=schema,
        adherence=JsonAdherence(strict_only_json=True, retries=1),
        on_token=callbacks.on_token,
        on_invalid_path=callbacks.on_invalid_path,
        stop_on_invalid=False,
    )
    print("\nAttempts:", result.attempts, " tokens:", len(emitted))
    return result

StreamCallbacks let you observe every token while the incremental monitor enforces the schema. Leave stop_on_invalid=True for hard stops, or set it to False to keep streaming after the warning while JSON retries repair the output.

Persona steering

import mlx.core as mx
from mlx_genkit import LogitBiasHook
H = model.args.hidden_size
model['_persona_v'] = mx.random.normal((H,)) * (1.0/(H**0.5))
cfg = GenerationConfig(max_tokens=64, temperature=0.7)
out = generate(model, tokenizer, 'Summarize MLX', cfg, hooks=[LogitBiasHook(param_key='_persona_v', alpha=1.2)])

Training (MLX)

from mlx_genkit import TrainingConfig, train_step, SoftPromptHook
from mlx.optimizers import AdamW
pad_id = getattr(tokenizer, 'pad_token_id', -100) or -100
opt = AdamW(learning_rate=2e-4)
batch = {'tokens': ...}  # mx.array [B, T]
cfg = TrainingConfig(dtype='bf16', loss_scale=1024.0)
loss = train_step(model, batch, opt, cfg, hooks=[SoftPromptHook(n_virtual=10, param_key='_soft_prompt')], pad_id=pad_id)

Utilities

from mlx_genkit import sequence_logprob, token_kl, ema_update, build_action_mask

# Per-sample mean log-prob on supervised positions (labels == -100 are ignored)
lp = sequence_logprob(model, batch_tokens, labels)  # [B]

# KL(pi || pref) averaged over supervised positions
kl = token_kl(model, ref_model, batch_tokens, labels)  # [B]

# EMA update of a target model from a source model
ema_update(target_model, model, decay=0.999)

# Supervised mask after prompt
mask = build_action_mask(prompt_lens=[12, 20], seq_len=T)  # [B, T] bool

Parity testing

• Torch vs MLX: python -m mlx_genkit.tests.parity_hf --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b --prompt 'hello'
• Suite (8 prompts): python -m mlx_genkit.tests.parity_suite --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b

CLI wrapper

mlxgk-generate \
  --model Qwen/Qwen3-0.6B \
  --prompt "Hello MLX" \
  --max-tokens 64 --temp 0.7 --top-p 0.95 \
  --num-beams 1 --no-repeat-ngram-size 2

Structured CLI flags

• --json-schema schema.json enable schema validation (optionally combine with --retries 2).
• --strict-only-json forbid commentary; --stream prints incremental output while validating.
• --validator jsonschema --validator mypkg.validators:custom layer pluggable validators.
• --semantic-checks checks.json applies must_contain, enum_in, and regex_on_field predicates.
• --log-jsonl adherence.jsonl --log-raw-on-fail capture adherence diagnostics per run.
• --backend selects the decoding backend (mlx, transformers, or vllm) and validates grammar support.

CLI chat and stop strings

• Chat: --messages-json '[{"role":"user","content":"hi"}]' (auto-applies template)
• Auto chat for plain prompts: add --auto-chat (or disable with --no-auto-chat); optional --system "You are helpful"
• Force treating plain prompts as user messages: --assume-user-chat (equivalent to --auto-chat)
• Stop strings: use --stop or the alias --stop-strings (comma-separated)

Defaults

• The CLI will, by default, auto-apply chat templates when the loaded tokenizer exposes a chat template (has apply_chat_template and a non-empty chat_template). Use --no-auto-chat to turn this off.

Prefetch and convert (download)

# Download an HF repo and convert to MLX format without loading into memory.
# Prints the local path, e.g., ./mlx_cache/Qwen_Qwen2-7B-Instruct
mlxgk-download --model Qwen/Qwen2-7B-Instruct

# Options
#  --cache-dir DIR           Cache location (default: ./mlx_cache)
#  --quantize                Quantize during conversion
#  --trust-remote-code       Allow custom code from the repo
#  --force                   Reconvert and overwrite existing cache

Adherence eval harness

mlxgk-eval --suite adherence_suite.yaml --markdown report.md --json report.json

Example suite (adherence_suite.yaml):

name: adherence_smoke
model: Qwen/Qwen3-0.6B
cases:
  - name: summary
    prompt: "Return a JSON object with summary and confidence"
    json_schema:
      type: object
      properties:
        summary: {type: string}
        confidence: {type: number}
      required: [summary, confidence]
    retries: 2
    strict_only_json: true

Performance bench

python -m mlx_genkit.tests.perf_bench --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b --prompt "Hello performance" --max-tokens 64

Releases

• Bump version across files (defaults to patch):
  • make bump-version (use PART=minor or PART=major to override)
• Create and push a git tag (vX.Y.Z):
  • make git-release
  • This tags and pushes the repo; PyPI packaging can be added later.

Notes

• Parity targets control‑surface equivalence: constraints, stops, finish reasons, determinism; token streams may differ across frameworks/devices.
• Sampling fast path reuses mlx-lm’s decoding loop and caches for best performance on Apple Silicon.

Known limitations

• Residual injection uses Python-level patching; highly optimized/compiled paths may bypass it. Use forward_with_hidden(..., strict=True) when you need deterministic capture/injection semantics.
• Some MLX model classes may not accept input_embeddings (used for soft prompts in training). In those cases, the library now falls back gracefully to standard token-only forward.
• Beam search applies processors on raw logits and then normalizes (HF behavior). Earlier parity reports in this repo may reflect the previous implementation on normalized logprobs.

Tips

• When running examples directly from the repo, make sure you’re using the local sources: pip install -e . or run with PYTHONPATH=..
• Parity/perf harnesses will download HF models; ensure network access and sufficient disk space.