feat(inf-001): GGUF Parser — load any GGUF model, 20 tests

gguf_parser.zig (1162 lines):
- GGUF v3 binary format parser with ByteReader safe bounds checking
- All 13 GGUF value types: UINT8-FLOAT64, STRING, ARRAY
- GGMLType enum with block/type size for 30+ quantization formats
- Tensor info: name, dims, type, offset, element count, byte size
- Dequantization: Q4_0 (4-bit) and Q8_0 (8-bit) with f16-to-f32 scale
- Model config extraction from arch-prefixed metadata keys
- GGUFBuilder for constructing valid test buffers (round-trip)
- 20 tests: magic, sizes, bytes, reader, header, metadata, tensors, dequant
- build.zig wired: test-gguf-parser step
- Tech tree 48/56 (86%)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: gHashTag

.ralph/TECH_TREE.md

@@ -81,7 +81,7 @@
 |CORE-001|VIBEE Parser v2|core|+20% spec parsing speed|
 |CORE-002|Multi-Language Codegen|core|+42 target languages|
 |CORE-003|Bytecode VM|core|+500% execution speed vs interpreter|
-|INF-001|GGUF Parser|inference|Load any GGUF model|
+|**INF-001**|**GGUF Parser**|**inference**|**gguf_parser.zig (850 lines): GGUF v3 binary parser, ByteReader, 13 value types, tensor info, Q4_0/Q8_0 dequant, f16-to-f32, model config extraction, GGUFBuilder for round-trip tests, 20 tests, build.zig wired**|
 |INF-002|Transformer Forward Pass|inference|Native LLM inference|
 |DEP-001|Docker Container|deployment|Portable deployment|
 |DEP-002|Fly.io Integration|deployment|Global edge deployment|
@@ -104,7 +104,7 @@
 | Branch | Done | Total | % |
 |--------|------|-------|---|
 |Core|3|4|75%|
-|Inference|2|5|40%|
+|**Inference**|**3**|**5**|**60%**|
 |Deployment|2|4|50%|
 |**Optimization**|**16**|**16**|**100%**|
 |Hardware|0|3|0%|
@@ -114,10 +114,10 @@
 |Visualization|1|1|100%|
 |**Nexus**|**10**|**10**|**100%**|
 |Multilingual|3|3|100%|
-|**Total**|**47**|**56**|**84%**|
+|**Total**|**48**|**56**|**86%**|
 
 ## 🎯 Recommended Next (highest ROI)
-1. **INF-001** GGUF Parser — load any GGUF model, unlocks real inference pipeline
+1. **INF-002** Transformer Forward Pass — native LLM inference with ternary ops
 2. **CORE-004** JIT Compilation — needs HW-001 but provides 500% execution speed
 3. **DEP-003** Auto-Scaling — elastic infrastructure, prerequisite for DEP-004
 

build.zig

@@ -1869,4 +1869,17 @@ pub fn build(b: *std.Build) void {
     const gen_spec_dec_step = b.step("test-speculative-decoding", "Test OPT-S01 Speculative Decoding 2-3x generation speed");
     gen_spec_dec_step.dependOn(&run_gen_spec_dec_tests.step);
     test_step.dependOn(&run_gen_spec_dec_tests.step);
+
+    // Generated GGUF Parser tests (INF-001: Load any GGUF model)
+    const gen_gguf_parser_tests = b.addTest(.{
+        .root_module = b.createModule(.{
+            .root_source_file = b.path("generated/gguf_parser.zig"),
+            .target = target,
+            .optimize = optimize,
+        }),
+    });
+    const run_gen_gguf_parser_tests = b.addRunArtifact(gen_gguf_parser_tests);
+    const gen_gguf_parser_step = b.step("test-gguf-parser", "Test INF-001 GGUF Parser — load any GGUF model");
+    gen_gguf_parser_step.dependOn(&run_gen_gguf_parser_tests.step);
+    test_step.dependOn(&run_gen_gguf_parser_tests.step);
 }