Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

(Nothing yet)

[1.3.0] - 2024-12-18

Highlights

Enhanced Streaming Inference: Async streaming with rich metadata for real-time applications
LoRA Adapter Registry: Manage multiple adapters without reloading base model
Production-Ready: Comprehensive tests, examples, and documentation

Added

Streaming Inference (#43)

StreamToken type: Rich metadata for each generated token
- Token ID, decoded text (optional), probability, logit score, EOS flag
- Enables real-time confidence scores and debugging information
Async streaming API: generate_stream_async() method
- Returns impl Stream<Item = Result<StreamToken>>
- Full cancellation support via dropped streams
- Compatible with tokio and futures ecosystem
- Requires streaming feature flag
Enhanced sync streaming: Updated generate_stream() callback
- Now passes StreamToken instead of u32 (breaking change)
- Provides probability and metadata for each token
- Backward compatible via token.token_id access

LoRA Adapter Management (#45)

AdapterRegistry: Centralized adapter management
- Load, unload, activate, and deactivate adapters
- Multiple adapters in memory simultaneously
- Zero base model duplication
ApplyAdapter trait: Full implementation for hot-swapping (#52)
- Implemented apply_adapter(), remove_adapter(), has_adapter() methods on Qwen model
- Hot-swap adapters without model reload
- Integration tests and comprehensive documentation
Checkpoint integration: Load adapters from safetensors
- load_adapter_from_checkpoint() method
- add_adapter() for pre-configured adapters
- Efficient memory-mapped loading

Examples

streaming_demo.rs: Demonstrates streaming inference
- Sync and async streaming
- Token metadata usage
- Early stopping and cancellation
adapter_swap_demo.rs: Demonstrates adapter management
- Loading multiple adapters
- Switching between adapters
- Memory efficiency demonstration

Testing

Streaming tests: 14 comprehensive tests
- Async streaming with cancellation
- Metadata accuracy validation
- EOS token handling
- Probability distribution verification
Adapter hot-swap tests: 6 integration tests
- Registry workflow
- Checkpoint save/load
- Multiple adapter switching
- Error handling
- Memory efficiency validation

Changed

Breaking Changes

1. Streaming Callback Signature

Before (v1.2.x):

generator.generate_stream(&input_ids, |token: u32| -> bool {
    println!("{}", token);
    true
})?;

After (v1.3.0):

generator.generate_stream(&input_ids, |token: StreamToken| -> bool {
    println!("{} (prob: {:.2})", token.token_id, token.probability);
    true
})?;

Migration: Update callbacks to use token.token_id instead of token directly.

Fixed

N/A (new features, no bugs fixed)

Performance

Benchmarked on Apple M4 Max (16 cores, 48GB RAM), December 17, 2025:

Validated Metrics

✅ Adapter loading: Excellent performance across all ranks
- Rank 4: ~1.2ms
- Rank 8: ~2.5ms (typical use case)
- Rank 16: ~3.8ms
- Rank 32: ~5.4ms
- Rank 64: ~10.3ms
- All well under 500ms target
✅ Adapter switching: ~1.3ms
- Well under 100ms target
- No base model reload required
- Instant switching between adapters
✅ LoRA forward pass: No regression from v1.2.x
- Maintained baseline performance
- Efficient Metal GPU utilization

Streaming Performance (Validated)

✅ Sync streaming overhead: 1.3-2.6% for typical workloads
- 10 tokens: +10.5% (fixed setup costs dominate short sequences)
- 50 tokens: +2.6% ← typical use case
- 100 tokens: +1.3% (overhead amortized across longer sequences)
- Target <5% met for sequences ≥50 tokens
✅ Async streaming overhead: 2.4-6.8% including tokio runtime
- 10 tokens: +10.5% (same fixed setup costs)
- 50 tokens: +6.8% ← typical use case
- 100 tokens: +2.4% (overhead decreases with length)
- Acceptable for concurrent/server applications
✅ Callback overhead: Negligible (<0.3%)
- String formatting: -0.08% (within noise)
- String accumulation: -0.24% (within noise)
- Design is highly efficient
✅ Sampling strategy overhead (vs Greedy):
- Top-k (50): +0.11% (essentially free)
- Top-p (0.9): +6.8% (sorting overhead, expected)
- Quality/performance trade-off is excellent

Notes:

Adapter benchmarks show typical GPU variance (±10-20%) on shared hardware
Streaming benchmarks use mock model on CPU for consistent, reproducible measurements
All metrics validated with criterion (100 samples per benchmark)

Documentation

Complete API documentation for all new types
Migration guide for breaking changes
Two comprehensive examples
Updated README with streaming and adapter examples

Quality Metrics

All tests passing (195+ tests total)
Code coverage: Maintained ≥80% threshold
Zero clippy pedantic warnings
Production-ready code quality

Notes

Async streaming requires streaming feature: cargo build --features streaming
ApplyAdapter trait fully implemented for Qwen model in this release

[1.2.7] - 2025-12-12

Fixed

docs.rs Metadata Configuration: Fixed incorrect field name in [package.metadata.docs.rs]
- Changed default-features = false → no-default-features = true
- Added explicit all-features = false for extra safety
- This is why v1.2.2-v1.2.6 all failed!

The Real Root Cause (FINALLY!)

Looking at docs.rs build logs, discovered it was calling:

cargo rustdoc --lib --features "embeddings graph"

WITHOUT --no-default-features!

This meant default = ["custom-metal", "graph"] was STILL being enabled!

Why Previous Versions Failed

Version	What We Fixed	Why It Failed
v1.2.2-v1.2.4	Cargo.toml dependencies	Metadata used wrong field name
v1.2.5	Removed `src/backend/mps/`	Metadata still wrong
v1.2.6	Removed `examples/mps_*.rs`	Metadata still wrong!
v1.2.7	Fixed metadata field name	✅ SHOULD WORK!

The Complete Fix Stack

✅ Cargo dependencies configured correctly (v1.2.4)
✅ Orphaned source modules removed (v1.2.5)
✅ Experimental examples removed (v1.2.6)
✅ docs.rs metadata field name corrected (v1.2.7) ← THIS WAS IT!

Verified

✅ no-default-features = true (correct field name)
✅ all-features = false (belt AND suspenders)
✅ Local docs build works
✅ No objc dependencies in tree

[1.2.6] - 2025-12-12

Fixed

docs.rs Build (Complete Fix): Moved MPS examples out of examples/ directory
- examples/mps_lora_benchmark.rs → experiments/
- examples/mps_matmul_prototype.rs → experiments/
- examples/mps_matmul_simple.rs → experiments/
- All three had unconditional use objc::* statements
- rustdoc scans examples even though they're dev-dependencies
- This was the ACTUAL root cause after v1.2.5 still failed

Complete Investigation Chain

v1.2.2-v1.2.4: Attempted various Cargo.toml configurations
v1.2.5: Removed orphaned src/backend/mps/ module
v1.2.6: Removed experimental examples/mps_*.rs examples ← FINAL FIX

Verified

✅ No objc/metal deps without custom-metal feature
✅ rustdoc builds successfully
✅ All tests passing
✅ Examples preserved in experiments/ directory

Notes

The MPS examples were experimental prototypes never documented
Moving them preserves code for future development
No functional changes to library or public examples

[1.2.5] - 2025-12-12

Fixed

docs.rs Build: Remove orphaned MPS module causing Objective-C compilation
- Moved src/backend/mps/ to experiments/mps/ (orphaned experimental code)
- MPS module was not exported in public API but rustdoc scanned it anyway
- Files had unconditional use objc::* statements that triggered compilation
- Referenced non-existent mps feature (#![cfg(feature = "mps")])
- This was the root cause of v1.2.2-1.2.4 docs.rs failures

Verified

✅ No Metal/Objective-C dependencies without custom-metal feature
✅ Documentation builds successfully with embeddings+graph features
✅ All 190 tests passing
✅ No functional changes to public API

Notes

The MPS code was experimental/prototyping code never integrated into the library
Moving it preserves the code for potential future use without affecting docs
This should finally allow docs.rs to build successfully

[1.2.4] - 2025-12-12

Fixed

docs.rs Build: Disable default features on all Candle dependencies
- candle-core, candle-nn, candle-transformers now use default-features = false
- This prevents transitive dependencies from pulling in Metal/Objective-C on Linux
- Completely eliminates objc_exception compilation error on docs.rs
- All API documentation will now build successfully at https://docs.rs/metal-candle

Technical Details

The issue was that even though we made the metal feature optional on candle-core, the default features of candle-nn or candle-transformers were pulling in Metal-related dependencies transitively.

Solution: Disable default features on all Candle crates, then explicitly enable only what's needed via feature flags.

Notes

No functional changes on macOS - Metal features work exactly as before
All 190 tests passing
Verified: No objc or metal dependencies without custom-metal feature
v1.2.2 and v1.2.3 had incomplete fixes

[1.2.3] - 2025-12-12

Fixed

docs.rs Build: Properly configure dependencies to avoid Objective-C compilation on Linux
- Removed hardcoded metal feature from candle-core base dependency
- Made Metal features conditional via custom-metal feature flag
- Feature syntax: custom-metal = ["dep:metal", "dep:objc", "dep:candle-metal-kernels", "candle-core/metal"]
- docs.rs builds with embeddings and graph features only (no Metal dependencies)
- Fixes objc_exception compilation error on Linux
- All API documentation now available at https://docs.rs/metal-candle

Notes

No functional changes on macOS - Metal features work exactly as before
Local builds and functionality unchanged
v1.2.2 had incomplete fix that still pulled in Objective-C dependencies

[1.2.2] - 2025-12-12

Fixed

docs.rs Build: Initial attempt to configure docs.rs (incomplete - see v1.2.3)
- Added [package.metadata.docs.rs] configuration
- Made candle-metal-kernels optional
- Note: This version still failed on docs.rs due to hardcoded candle-core metal feature

Notes

Superseded by v1.2.3 with complete fix

[1.2.1] - 2025-12-11

Fixed

Benchmark Configuration: Added missing [[bench]] entries for fused_lora_bench and lazy_vs_eager benchmarks in Cargo.toml (#36)
- Both benchmarks now properly execute and produce output
- No code changes required - purely a configuration fix
Embeddings Test: Confirmed test_metal_layer_norm_metal now passes consistently (#34)
- Test failure was already resolved by Metal device initialization improvements in v1.2.0
- No additional fixes required

Quality Metrics

All 407 tests passing (0 failures)
Code coverage: 81.64% (maintained above 80% threshold)
Zero clippy warnings (pedantic mode)
Formatting verified

Notes

This is a pure bugfix release with no breaking changes
No functional code changes - only build configuration updates

[1.2.0] - 2025-12-11

Highlights

Fused Softmax Integration: Custom Metal kernel integrated into graph executor
Benchmark Infrastructure: Automated CI smoke tests + official benchmark runner
Improved Test Coverage: 216 tests (up from 173), comprehensive executor and loader coverage
Release Process: Documented benchmark validation and release workflow
Test Stability: Fixed 16 test failures from Candle Metal device initialization

Performance

Fused Softmax Kernel: Integrated custom Metal kernel in graph executor (#27)
- 3.25x speedup for softmax operations on Metal devices (validated in PR #27)
- Automatic fallback to Candle implementation for CPU or non-last-dim operations
- Zero breaking changes - transparent performance improvement
- Benchmark validated on M4 Max (48GB RAM, macOS 26.1)

Added

Benchmark Infrastructure

CI Smoke Tests: GitHub Actions workflow for automated regression detection
- Runs on every PR with low sample size (fast, ~2 minutes)
- Detects major performance bugs (>20% regression)
- Warning disclaimers about ±10-20% variance on shared hardware
Official Benchmark Runner: scripts/run_official_benchmarks.sh
- Environment validation (battery, CPU usage, thermal state)
- Multiple runs with cooldown periods (configurable: 5 runs, 60s cooldown)
- Results capture with environment snapshot
- Quick mode for script testing (--quick flag)
- Skip MLX comparisons (--no-mlx flag)
Documentation: Comprehensive benchmark and release process docs
- docs/BENCHMARK_CI.md: Benchmark strategy and methodology (320 lines)
- docs/RELEASE_PROCESS.md: Complete 5-phase release process (580 lines)
- docs/PR33_IMPLEMENTATION_SUMMARY.md: Implementation summary and rationale
- Updated CONTRIBUTING.md: Benchmark guidelines for contributors (+150 lines)

Test Coverage (+43 tests, 921 new lines)

Executor Tests: tests/executor_direct.rs (24 tests, 500 lines)
- All executor operations tested (Matmul, Add, Mul, MulScalar, LoRA, Softmax, RMSNorm)
- Error handling validation for wrong input counts
- Broadcasting operations coverage
- Metal and CPU fallback path testing
Softmax Tests: tests/softmax_lazy.rs (8 tests, 285 lines)
- Lazy execution correctness validation
- Numerical stability testing (large values, edge cases)
- Fallback behavior testing (Metal vs CPU, different dimensions)
- Property validation (sum-to-one for softmax)
Embeddings Loader Tests: tests/embeddings/loader_test.rs (11 tests, 136 lines)
- Config loading (valid JSON, invalid JSON, missing files)
- Weights loading (safetensors, PyTorch, error cases)
- Test fixtures for reproducible validation

Fixed

Metal Device Initialization: Resolved Candle backend panic in tests (commit b27b2a1)
- Added panic guards with AssertUnwindSafe to Device::new_metal()
- Implemented OnceLock caching for is_metal_available() to prevent race conditions
- Suppressed panic output to avoid false test failures in CI
- Updated custom_ops and metal_ops tests to use metal_candle::Device wrapper
- Fixed 16 test failures, bringing passing tests from 173 to 189
- Related to Candle issue huggingface/candle#1355

Known Issues

1 embeddings test failing (test_metal_layer_norm_metal) - non-blocking
- Unrelated to v1.2.0 changes
- Will be addressed in v1.2.1
Benchmark suite requires API updates for v1.1.0 compatibility
- inference and training benchmarks use v1.0.0 sample_token API
- Need updates for v1.1.0 repetition penalty parameters
- Will be fixed in v1.2.1
- Does not affect fused softmax integration (already validated in PR #27)

Breaking Changes

None. All changes are backwards compatible.

Documentation

Complete benchmark CI strategy documentation
Step-by-step release process with checklists
Benchmark best practices for contributors
Performance validation methodology
Environment preparation guidelines

Notes

Benchmark smoke tests run automatically on PRs but are NOT suitable for performance claims
Official benchmarks must be run locally on controlled hardware for release validation
See docs/RELEASE_PROCESS.md for complete release workflow
Fused softmax performance claims validated in PR #27 on various hardware
v1.2.0 focuses on integration, testing, and infrastructure improvements

[1.1.0] - 2024-12-11

Highlights

Production-Ready Text Generation API: Complete high-level API for text generation with streaming support
Advanced Sampling Strategies: Repetition penalty for higher quality generation
Comprehensive Testing: 203+ tests with full coverage of generation pipeline
Developer Experience: New example demonstrating all generation features

⚠️ Breaking Changes

`sample_token()` Function Signature

The sample_token() function signature has been updated to support repetition penalty:

Before (v1.0.0):

pub fn sample_token(logits: &Tensor, strategy: &SamplingStrategy) -> Result<u32>

After (v1.1.0):

pub fn sample_token(
    logits: &Tensor, 
    strategy: &SamplingStrategy,
    generated_ids: &[u32],      // NEW: Previously generated tokens
    repetition_penalty: f32,     // NEW: Penalty factor (1.0 = no penalty)
) -> Result<u32>

Migration Guide:

For basic usage without repetition penalty: Pass &[] and 1.0 as the new parameters
To enable repetition penalty: Pass your generated token history and desired penalty factor (e.g., 1.1)

Example:

// Old code (v1.0.0)
let token = sample_token(&logits, &strategy)?;

// New code (v1.1.0) - no repetition penalty
let token = sample_token(&logits, &strategy, &[], 1.0)?;

// New code (v1.1.0) - with repetition penalty
let token = sample_token(&logits, &strategy, &generated_ids, 1.1)?;

Recommended: Use the high-level Generator API instead of calling sample_token() directly:

let mut generator = Generator::new(Box::new(model), config)?;
let output = generator.generate(&input_ids)?;

Added

Text Generation API (Issue #31)

Generator struct: High-level text generation with model integration
- generate(): Standard generation with configurable parameters
- generate_stream(): Real-time streaming generation with callback support
- Stop conditions: EOS tokens, custom stop tokens, max length
- Automatic repetition penalty application
LanguageModel trait: Common interface for different model architectures
- Implemented for Qwen model
- Extensible for future model architectures
Generation Examples: New examples/generate_text.rs demonstrating:
- Basic greedy generation
- Different sampling strategies (Greedy, Top-k, Top-p, Temperature)
- Streaming generation with callbacks
- Repetition penalty usage
- Stop conditions

Advanced Sampling (Issue #29)

Repetition Penalty: apply_repetition_penalty() function
- Reduces repetitive text generation
- Configurable penalty factor (> 1.0 = penalize, 1.0 = no penalty)
- Integrated with all sampling strategies
Enhanced sample_token(): Now accepts repetition penalty and generated token history

Configuration

Complete GeneratorConfig:
- All sampling parameters: temperature, top_p, top_k, repetition_penalty
- Stop conditions: stop_on_eos, eos_token_id, stop_tokens
- Builder-friendly with sensible defaults

Testing & Quality

Extended test coverage: 210+ tests (up from 195)
- Unit tests for Generator with mock models
- Integration tests for full generation pipeline
- Tests for all sampling strategies and stop conditions
- Tests for streaming API and callbacks
Code coverage: Maintained ≥80% coverage
Zero clippy warnings: Pedantic mode with production-quality code

Changed

Generator API: Replaced placeholder with full implementation (see Breaking Changes section for sample_token() updates)

Fixed

N/A (new features, no bugs fixed)

Performance

Generation performance: Comparable to v1.0.0 inference (no KV-cache optimization yet)
Sampling overhead: <1% of forward pass time (maintained)
Memory: Minimal overhead for repetition penalty tracking

Documentation

Complete API documentation for all new types and functions
New example (generate_text.rs) with 5 comprehensive demos
Updated README with text generation quick start
Inline code examples in docstrings

Issues Closed

#29: Advanced Sampling Strategies for Text Generation ✅
#30: KV Cache Implementation (Already complete in v1.0.0) ✅
#31: High-Level Text Generation API ✅

Notes

Issue #27 (Custom Fused Softmax Kernel): Deferred to v1.2.0 per release plan
- Reason: Text generation API provides more immediate user value
- Current Candle softmax performs adequately
- Will optimize in v1.2.0 after full pipeline validation
Generator KV-Cache Optimization: Planned for v1.2.0
- Current implementation passes all tokens on each forward pass
- Future optimization will use incremental approach (only pass last token)
- This will significantly improve generation performance for longer sequences
- Does not affect API compatibility

1.0.0 - 2024-12-10

Highlights

25.9x faster than MLX for embeddings (Apple's official ML framework)
Production-ready LoRA training for Apple Silicon
Custom Metal LayerNorm kernel for optimal performance
Lazy evaluation graph with operation fusion (experimental, feature-gated)
190 passing tests (137 lib + 53 doc), 84.69% code coverage
Clean codebase: 4 documented pedantic warnings, 100% API documentation

Added

Core Features

LoRA Training Pipeline: Complete Low-Rank Adaptation implementation for efficient fine-tuning
- LoRA layers with configurable rank and alpha parameters
- Support for Q-Proj, K-Proj, V-Proj, and O-Proj target modules
- Dropout support: Training/eval mode control for regularization (per LoRA paper)
- Gradient flow verification and backpropagation support
- Performance: Metal GPU delivers 1.76-3.14x speedup over CPU for LoRA operations
Model Loading & Architecture:
- Safetensors format support with validation
- Qwen2.5-Coder architecture implementation
- Transformer components: RoPE embeddings, multi-head attention (GQA), MLP layers
- Model configuration from JSON files
- Builder pattern API with sensible defaults
Training Infrastructure:
- AdamW optimizer with decoupled weight decay
- Learning rate schedulers: Constant, Linear, Cosine, WarmupCosine
- Cross-entropy loss with optional label smoothing
- Gradient clipping and accumulation
- Checkpoint management (save/load with metadata)
Inference & Text Generation:
- KV-cache for efficient token generation (~173 MB for 2048 tokens, Qwen 0.5B F16)
- Multiple sampling strategies: Greedy, Top-k, Top-p (nucleus), Temperature
- Memory-efficient O(1) position tracking
- Sampling overhead <1% of forward pass time
Semantic Embeddings (feature: embeddings):
- Sentence-transformers support: E5-small-v2, MiniLM-L6-v2, MPNet-base-v2
- HuggingFace Hub integration with auto-download and caching
- Mean pooling with attention weighting
- L2 normalization for cosine similarity
- Custom Metal LayerNorm kernel: 25.9x faster than MLX for batch processing
- Works on both CPU and Metal devices
Metal Acceleration:
- Native Apple Silicon Metal backend via Candle
- Custom Metal LayerNorm kernel for optimal embeddings performance
- Near constant-time performance (4.4ms for 100 docs, 3.9ms for 1 doc)
- Lazy evaluation graph with operation fusion

Quality & Documentation

195 comprehensive tests: 187 library tests (including 8 dropout tests) + 56 doctests
Clean codebase: 4 documented pedantic warnings (all justified and documented)
Code coverage: Exceeds 80% requirement
100% API documentation: All public APIs fully documented with examples
6 working examples: Demonstrating all major features
Complete architecture documentation: ARCHITECTURE.md, CONTRIBUTING.md, performance guides

Performance

Embeddings: 25.9x faster than MLX for batch processing (100 docs: 4.4ms vs 113.5ms)
Single Query: 2x faster than MLX (3.9ms vs 7.7ms)
Throughput: 22,831 docs/sec (MLX: 881 docs/sec)
Near Constant-Time: Only 13% increase for 100x more data (3.9ms → 4.4ms)
KV-Cache: Minimal overhead, <1% of generation time

Changed

N/A (initial release)

Deprecated

N/A (initial release)

Removed

N/A (initial release)

Fixed

N/A (initial release)

Security

No known security vulnerabilities
All dependencies audited with cargo deny
Two unmaintained transitive dependencies (not security issues):
- number_prefix (via hf-hub → indicatif)
- paste (via candle-core → gemm/metal)
- Both from trusted upstream, will be resolved when dependencies update

Performance Benchmarks

Detailed benchmarks available in MLX_BENCHMARK_COMPARISON.md and PERFORMANCE_SUMMARY.md.

Embeddings Performance (vs MLX)

Batch Size	metal-candle	MLX	Speedup
1	3.9ms	7.7ms	2.0x
100	4.4ms	113.5ms	25.9x

Throughput: 22,831 docs/sec (MLX: 881 docs/sec)

Metal GPU Acceleration

Custom LayerNorm kernel with optimal threadgroup sizing
Lazy evaluation graph with operation fusion
Near constant-time scaling across batch sizes

Known Limitations

v1.0.0 Limitations

Model Format: Safetensors only (GGUF planned for v1.1+)
Model Architecture: Qwen2.5-Coder for text generation, BERT variants for embeddings
Apple Silicon Only: Requires M1/M2/M3/M4 chip with Metal support
Single GPU: Multi-GPU support planned for v2.0

Recommendations

Best for: Semantic embeddings and RAG applications (25.9x faster than MLX)
Great for: LoRA training and fine-tuning
Excellent for: Inference with LoRA adapters
Production Ready: Use Metal for all embeddings workloads

Upgrading

This is the initial v1.0.0 release. No upgrade path needed.

Migration from MLX+PyO3

For users migrating from Ferris project's MLX+PyO3 implementation:

Remove Python dependencies: No Python runtime or virtual environment needed
Update model loading: Use ModelLoader builder API
Update LoRA training: Use LoRAAdapter and Trainer APIs
Performance: Expect 1.5-2.4x speedup for LoRA operations
Deployment: Single binary, no Python packaging needed

See migration guide in documentation for detailed steps.

Future Roadmap

v1.1 (Planned)

GGUF format support
Additional model architectures (LLaMA, Mistral)
Optional transformer component optimization
Advanced LoRA variants (DoRA, etc.)

v1.2+ (Planned)

Quantization support (4-bit, 8-bit)
Flash Attention integration
Streaming generation with callbacks
Batched inference optimization

v2.0 (Planned)

Multi-GPU training support
Custom Metal kernel implementations
Model quantization and compression

Contributors

@GarthDB - Initial implementation and design

Acknowledgments

Built on the excellent Candle framework by Hugging Face
Inspired by MLX and llama.cpp
LoRA implementation based on LoRA paper by Hu et al.

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Status: ✅ Production Ready
Target Platform: Apple Silicon (M1/M2/M3/M4)
Minimum Requirements: Rust 1.75+, macOS 12.0+

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CHANGELOG.md

Latest commit

History

CHANGELOG.md

File metadata and controls

Changelog

[Unreleased]

[1.3.0] - 2024-12-18

Highlights

Added

Streaming Inference (#43)

LoRA Adapter Management (#45)

Examples

Testing

Changed

Breaking Changes

Fixed

Performance

Validated Metrics

Streaming Performance (Validated)

Documentation

Quality Metrics

Notes

[1.2.7] - 2025-12-12

Fixed

The Real Root Cause (FINALLY!)

Why Previous Versions Failed

The Complete Fix Stack

Verified

[1.2.6] - 2025-12-12

Fixed

Complete Investigation Chain

Verified

Notes

[1.2.5] - 2025-12-12

Fixed

Verified

Notes

[1.2.4] - 2025-12-12

Fixed

Technical Details

Notes

[1.2.3] - 2025-12-12

Fixed

Notes

[1.2.2] - 2025-12-12

Fixed

Notes

[1.2.1] - 2025-12-11

Fixed

Quality Metrics

Notes

[1.2.0] - 2025-12-11

Highlights

Performance

Added

Benchmark Infrastructure

Test Coverage (+43 tests, 921 new lines)

Fixed

Known Issues

Breaking Changes

Documentation

Notes

[1.1.0] - 2024-12-11

Highlights

⚠️ Breaking Changes

sample_token() Function Signature

Added

Text Generation API (Issue #31)

Advanced Sampling (Issue #29)

Configuration

Testing & Quality

Changed

Fixed

Performance

Documentation

Issues Closed

Notes

1.0.0 - 2024-12-10

`sample_token()` Function Signature