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vibe_coding_router

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v4: retrained on 1,644 samples (16x more data), simpler architectures

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Files changed (7) hide show
  1. README.md +23 -15
  2. config.json +23 -23
  3. embedding_extractor.pkl +2 -2
  4. router_a.safetensors +2 -2
  5. router_b.safetensors +2 -2
  6. scaler.pkl +1 -1
  7. sweep_results.json +641 -641
README.md CHANGED
@@ -9,7 +9,7 @@ tags:
9
  library_name: mlx
10
  ---
11
 
12
- # Vibe Coding Router
13
 
14
  A three-tier cascaded router for coding tasks that routes prompts between:
15
 
@@ -21,37 +21,45 @@ A three-tier cascaded router for coding tasks that routes prompts between:
21
 
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  Two cascaded binary MLP routers trained with **Privileged Information Distillation (PID)**:
23
 
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- - **Router A** (local vs cloud): 70-dim input -> [128, 64] -> 1, dropout=0.2
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- - **Router B** (sonnet vs opus): 70-dim input -> [64, 32] -> 1, dropout=0.2
26
 
27
  Features: 38 handcrafted code features + 32 PCA-reduced sentence embeddings (all-MiniLM-L6-v2).
28
 
29
  ## Training
30
 
31
- - **Router A**: 100 samples with real (local, sonnet, opus) quality scores
32
- - **Router B**: 1,729 samples (100 main + 1,644 cloud-only with sonnet+opus scores)
33
  - **Judge**: GPT-5.4 scoring correctness, completeness, code quality, explanation
34
- - **Loss**: PID (reward-weighted CE + KL divergence)
35
  - **Label smoothing**: epsilon=0.05, cost-aware margin for Router B (cost_premium=0.03)
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- - **HP sweep**: 108 configurations, 3-way split (train/val/test)
 
37
 
38
- ## Routing Distribution
 
 
 
 
 
 
 
 
39
 
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  | Tier | Rate | Use Case |
41
  |------|------|----------|
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- | Local | 46.7% | Simple tasks, explanations, basic code gen |
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- | Sonnet | 20.0% | Medium complexity, standard debugging |
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- | Opus | 33.3% | Architecture, complex multi-file tasks |
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46
  ## Thresholds
47
 
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- - Router A: 0.526 (p(cloud) >= threshold -> route to cloud)
49
  - Router B: 0.474 (p(opus) >= threshold -> route to Opus, else Sonnet)
50
 
51
  ## Files
52
 
53
- - `router_a.safetensors` - Router A weights (128x64 MLP)
54
- - `router_b.safetensors` - Router B weights (64x32 MLP)
55
  - `config.json` - Model config, thresholds, training results
56
  - `scaler.pkl` - StandardScaler for feature normalization
57
  - `embedding_extractor.pkl` - PCA-reduced sentence-transformers extractor
@@ -61,7 +69,7 @@ Features: 38 handcrafted code features + 32 PCA-reduced sentence embeddings (all
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  ```python
62
  from router.three_tier_inference import ThreeTierRouter
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64
- router = ThreeTierRouter("models/three_tier_v3")
65
  tier, probs = router.route("Write a Python function to sort a list")
66
  # tier: "local", "sonnet", or "opus"
67
  ```
 
9
  library_name: mlx
10
  ---
11
 
12
+ # Vibe Coding Router v4
13
 
14
  A three-tier cascaded router for coding tasks that routes prompts between:
15
 
 
21
 
22
  Two cascaded binary MLP routers trained with **Privileged Information Distillation (PID)**:
23
 
24
+ - **Router A** (local vs cloud): 70-dim input -> [64, 32] -> 1, dropout=0.2
25
+ - **Router B** (sonnet vs opus): 70-dim input -> [32, 16] -> 1, dropout=0.0
26
 
27
  Features: 38 handcrafted code features + 32 PCA-reduced sentence embeddings (all-MiniLM-L6-v2).
28
 
29
  ## Training
30
 
31
+ - **Data**: 1,644 coding prompts with real quality scores from all three models
 
32
  - **Judge**: GPT-5.4 scoring correctness, completeness, code quality, explanation
33
+ - **Loss**: PID (reward-weighted CE + KL divergence), β_kl=0.02
34
  - **Label smoothing**: epsilon=0.05, cost-aware margin for Router B (cost_premium=0.03)
35
+ - **HP sweep**: 108 configurations, 3-way split (1150 train / 247 val / 247 test)
36
+ - **Thresholds**: calibrated on validation set only
37
 
38
+ ## Test Set Results
39
+
40
+ | Metric | Value |
41
+ |--------|-------|
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+ | Utility | 0.6349 |
43
+ | Regret | 0.0830 |
44
+ | vs Always-Opus | +0.63% utility, 40.9% cost savings |
45
+
46
+ ## Routing Distribution (test set)
47
 
48
  | Tier | Rate | Use Case |
49
  |------|------|----------|
50
+ | Local | 19.4% | Simple tasks, explanations, basic code gen |
51
+ | Sonnet | 21.5% | Medium complexity, standard debugging |
52
+ | Opus | 59.1% | Architecture, complex multi-file tasks |
53
 
54
  ## Thresholds
55
 
56
+ - Router A: 0.474 (p(cloud) >= threshold -> route to cloud)
57
  - Router B: 0.474 (p(opus) >= threshold -> route to Opus, else Sonnet)
58
 
59
  ## Files
60
 
61
+ - `router_a.safetensors` - Router A weights (64x32 MLP)
62
+ - `router_b.safetensors` - Router B weights (32x16 MLP)
63
  - `config.json` - Model config, thresholds, training results
64
  - `scaler.pkl` - StandardScaler for feature normalization
65
  - `embedding_extractor.pkl` - PCA-reduced sentence-transformers extractor
 
69
  ```python
70
  from router.three_tier_inference import ThreeTierRouter
71
 
72
+ router = ThreeTierRouter("models/three_tier_v4")
73
  tier, probs = router.route("Write a Python function to sort a list")
74
  # tier: "local", "sonnet", or "opus"
75
  ```
config.json CHANGED
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