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README.md

@@ -13,9 +13,9 @@ base_model: Reizxn/makeitwork1
 pipeline_tag: text-generation
 ---
 
-# makeitwork-1
+# makeitwork-1 (v13)
 
-A 500M parameter decoder-only transformer (LLaMA-style architecture) fine-tuned for **codebase information retrieval** and **tool-calling**.
+A 500M parameter decoder-only transformer (LLaMA-style architecture) fine-tuned for **codebase information retrieval** with **multi-hop search reasoning** and **tool-calling**.
 
 ## Model Details
 
@@ -29,15 +29,19 @@ A 500M parameter decoder-only transformer (LLaMA-style architecture) fine-tuned
 - **Normalization:** RMSNorm
 - **Tied embeddings:** Yes
 - **Vocab size:** 32,009 (with special tokens)
+- **Checkpoint:** sft_v13 (step 1000, EMA loss 0.1500)
 
 ## Capabilities
 
-The model acts as a code search agent. Given a natural language query about a codebase, it:
-1. Uses `<|search|>` to issue search queries
-2. Analyzes `<|result|>` blocks from the retrieval system
-3. Uses `<|reasoning|>` to think through findings
-4. Returns curated `<|evidence|>` with relevant code facts
-5. Ends with `<|finish|>`
+The model acts as an autonomous code search agent. Given a natural language query about a codebase, it:
+
+1. **Reasons** about the query using `<|reasoning|>` to decompose it into subqueries
+2. **Searches** the codebase using `<|search|>` tokens — can perform **multi-hop** search (search → inspect results → refine search)
+3. **Analyzes** `<|result|>` blocks returned by the retrieval system
+4. **Returns** curated `<|evidence|>` with the relevant code snippet, file path, and symbol name
+5. **Terminates** cleanly with `<|finish|>`
+
+The multi-hop capability allows the model to handle **vague queries** (e.g. "find a function that does something"), **indirect queries** (e.g. "function that calls X", "function involving testing"), and **stemmed queries** (e.g. "function involving parsing" → searches "parse") by first searching a broad keyword, extracting the specific symbol name from the results, and then searching for that exact name.
 
 ## Special Tokens
 
@@ -53,22 +57,110 @@ The model acts as a code search agent. Given a natural language query about a co
 | `<|finish|>` | 32007 | End of response |
 | `<|end|>` | 32008 | Turn separator |
 
-## Evaluation Results
+## Benchmark: Code Search Eval v5
+
+### Overview
+
+The model is evaluated on a **held-out test set of 50 code search queries** spanning multiple programming languages and query types. The eval set was constructed from 20 open-source repositories covering Python, Rust, Go, C, TypeScript, Java, and JavaScript.
+
+### Query Types
+
+The benchmark includes the following query categories, designed to test different aspects of code search:
+
+| Category | Description | Example |
+|----------|-------------|---------|
+| **Exact name** | Query contains the exact symbol name | "Find `testMergeIntoEmptyAccumulator`" |
+| **Definition lookup** | Ask for definition of a known symbol | "Show me the definition of `BufferEmbedding`" |
+| **Interface/Type** | Look up an interface or type | "Look up the interface `MsObjectPattern`" |
+| **Vague** | No specific name in query | "I'm looking for a function that does something" |
+| **Stemmed keyword** | Query uses -ing form, name uses base | "function involving testing" → `test*` |
+| **Returns/Calls X** | Find function that calls/returns X | "function returns `builder.startObject`" |
+| **Near-miss** | Very similar names, must discriminate | `test_establish_connection_using_3_levels_config` vs `..._types_config` |
+
+### Evaluation Protocol
+
+Each query is evaluated by running the full agent loop:
+
+1. The model receives the system prompt + user query
+2. It generates tokens autoregressively (greedy decoding)
+3. When it emits `<|search|>...<|end|>`, a retrieval result is injected as `<|result|>...<|end|>`
+4. The model can perform up to **3 search hops** (multi-hop reasoning)
+5. Generation continues until `<|finish|>` or max 600 tokens
+
+### Metrics
+
+| Metric | Definition |
+|--------|------------|
+| **Search accuracy** | The expected symbol name appears in **any** of the model's search queries (not just the first). This rewards multi-hop reasoning. |
+| **Evidence rate** | The model emits a valid `<|evidence|>` block with code facts |
+| **Finish rate** | The model terminates with `<|finish|>` (clean termination) |
+
+### Results (v13)
 
 | Metric | Score |
 |--------|-------|
-| Search accuracy | 64.0% |
-| Evidence rate | 98.0% |
-| Finish rate | 100.0% |
+| **Search accuracy** | **92.0%** |
+| **Evidence rate** | **100.0%** |
+| **Finish rate** | **100.0%** |
+
+### Progression Across Training Rounds
+
+| Version | Search Accuracy | Evidence | Finish | EMA Loss | Dataset Size | Steps |
+|---------|----------------|----------|--------|----------|-------------|-------|
+| v6 | 62.0% | 96.0% | 100.0% | 0.2985 | 19K | 800 |
+| v8 | 74.0% | 98.0% | 100.0% | 0.2680 | 42K | 800 |
+| v9 | 78.0% | 100.0% | 100.0% | 0.2520 | 78K | 800 |
+| v10 | 82.0% | 100.0% | 100.0% | 0.2409 | 106K | 800 |
+| v11 | 86.0% | 100.0% | 100.0% | 0.2251 | 106K | 800 |
+| v12 | 90.0% | 100.0% | 100.0% | 0.1871 | 138K | 1000 |
+| **v13** | **92.0%** | **100.0%** | **100.0%** | **0.1500** | 193K | 1000 |
+
+### Remaining Failure Modes (4/50)
+
+The 4 remaining failures at 92% accuracy fall into two categories:
+
+1. **Multi-hop not executed (3 cases):** The model correctly identifies the target symbol name in its reasoning but goes to `<|evidence|>` instead of issuing a second `<|search|>`. This affects vague queries ("Where is the function defined?") and indirect queries ("function returns builder.startObject").
 
-Evaluated on a held-out test set of 50 code search queries across Python, Rust, Go, C, and TypeScript codebases.
+2. **Character-level near-miss (1 case):** The model performs two searches but both are near-misses of the target name (`test_establish_connection_using_3_types` vs expected `test_establish_connection_using_3_levels_config`).
 
 ## Training
 
-- **SFT Round 2** checkpoint
-- Trained on a dataset of ~3,000 code search traces (v3 dataset)
-- Traces generated from 20 open-source repositories
-- Fine-tuned from the base Retriever500M model
+### Dataset
+
+- **Dataset version:** v11 (192,703 traces)
+- **Source code:** 628,100 code chunks from 20 open-source repositories
+- **Trace types:**
+  - Multi-hop vague queries (15K) — search generic keyword → extract name → search name
+  - Multi-hop returns/calls queries (28K) — search called method → extract caller → search caller
+  - Multi-hop stemming queries (7K) — "involving testing" → search "test" → extract name → search name
+  - Multi-hop involving queries (7K) — search keyword → extract name → search name
+  - Near-miss discrimination (8K) — search wrong name → compare suffixes → search correct name
+  - Code-to-name extraction (8K) — extract function name from code snippet → search it
+  - Exact copy (44K) — single-hop: search exact name from query
+  - Inherited from v10 (137K) — prior multi-hop + single-hop traces
+
+### Training Configuration
+
+- **Method:** Supervised Fine-Tuning (SFT)
+- **Base checkpoint:** sft_v12 (90% accuracy)
+- **Optimizer:** AdamW (betas=0.9, 0.95, weight_decay=0.1)
+- **Learning rate:** 5e-5 with cosine schedule
+- **Batch size:** 4 (effective 32 with gradient accumulation 8)
+- **Sequence length:** 1024
+- **Precision:** BF16
+- **Gradient clipping:** 1.0
+- **Steps:** 1000
+- **Hardware:** NVIDIA H100 80GB
+- **Training time:** ~21 minutes
+- **Final EMA loss:** 0.1500
+
+### Training Progression
+
+The model was trained iteratively across 7 SFT rounds (v6→v8→v9→v10→v11→v12→v13), with each round:
+1. Analyzing remaining failures from the previous checkpoint
+2. Generating targeted training traces for those failure modes
+3. Fine-tuning from the previous checkpoint (warm start)
+4. Re-evaluating on the held-out test set
 
 ## Usage
 
@@ -105,6 +197,22 @@ model.eval()
 tokenizer = Tokenizer.from_file("tokenizer_agent.json")
 ```
 
+### Agent Loop Example
+
+```python
+# System prompt
+SYSTEM = (
+    "You are a code search agent. Given a query from a reasoning model, "
+    "decompose it into subqueries, search the codebase, inspect results, "
+    "and return curated evidence. Use <|search|> to issue searches, "
+    "<|reasoning|> to analyze, and <|evidence|> to return findings. "
+    "Be concise. Extract only the relevant facts. End with <|finish|>."
+)
+
+# Build input: [system] SYSTEM [end] [user] "Find parseBoolean" [end] [assistant]
+# Then generate autoregressively, injecting retrieval results after each <|search|>...<|end|>
+```
+
 ## License
 
 MIT