mechramc
/

codek-qwen2.5-coder-7b-lora-v3

@@ -1,210 +1,130 @@
 ---
-base_model: unsloth/Qwen2.5-Coder-7B-Instruct
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Qwen2.5-Coder-7B-Instruct
 - lora
 - sft
-- transformers
-- trl
 - unsloth
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.18.1

 ---
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+- mechramc/codek-v1
+language:
+- en
 library_name: peft
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
 - lora
+- peft
 - sft
+- qwen2.5
+- qwen2.5-coder
+- code
+- reasoning
+- debugging
+- pedagogy
+- fine-tuned
 - unsloth
+- trl
+- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
 ---
+# CodeK v3 — Qwen2.5-Coder-7B LoRA
+A LoRA adapter fine-tuned on **CodeK**, a synthetic dataset of Python programming tasks
+written in the style of Andrej Karpathy's open-source code. The model is trained to reason
+carefully about code: explaining implementations, diagnosing bugs, contrasting correct vs.
+incorrect versions, and generating multi-hypothesis debugging chains.
+**Best checkpoint:** `checkpoint-800` (eval loss: 0.5888)
+---
 ## Model Details
+| Field | Value |
+|-------|-------|
+| Base model | `Qwen/Qwen2.5-Coder-7B-Instruct` |
+| Adapter type | LoRA (rank 16, alpha 32, RSLoRA) |
+| Target modules | q/k/v/o proj, gate/up/down proj |
+| Training tokens | response-only (prompt tokens masked) |
+| Best checkpoint | checkpoint-800 |
+| Eval loss | 0.5888 |
+| Training hardware | NVIDIA A100 80GB SXM4 |
+---
+## Training Data
+The CodeK v3 dataset combines **v2** (398 seeds) and **v3** (161 seeds) augmentation pipelines
+for a total of **559 unique Python tasks** across 9 categories:
+- Data structures, algorithms, graphs, dynamic programming
+- Numerical methods, parsing, concurrency, bit manipulation, compression
+Each seed is augmented across up to 5 passes:
+| Pass | Type | Description |
+|------|------|-------------|
+| Pass 1 | Reasoning | Step-by-step explanation of the correct implementation |
+| Pass 2 | Debugging | Single-line surgical bug + model diagnosis (via Codex, 100% coverage) |
+| Pass 3 | Contrast | Correct vs. incorrect comparison with explanation |
+| Pass 4 | Research loop | Multi-turn investigation of the implementation |
+| Pass 5 | Multi-hypothesis | Competing bug hypotheses, ranked by plausibility |
+**Training split:** 6,757 pairs (504 seed-level train tasks)
+**Validation split:** 728 pairs (55 seed-level held-out tasks, zero task overlap with train)
+### Key improvements over v2 model
+- **Seed-level val split** — validation set has no task overlap with training (eval loss is meaningful)
+- **Response-only loss** — prompt tokens masked; model only trained on assistant responses
+- **Pass 5** — multi-hypothesis bug reasoning signal (new in v3)
+- **Pass 2 via Codex** — 100% pass 2 coverage with sharper `change_token` annotations
+- **`change_token` field** — targets the `change_hit` failure mode from the v1/v2 evals
+---
 ## Evaluation
+Ground-truth Pass 2 eval on 50 held-out v1 seeds (same seeds used across all versions for apples-to-apples comparison). A prediction passes if it correctly identifies both the **function** containing the bug and the **nature of the change**.
+| Version | Dataset | LoRA Pass@1 | Base Pass@1 |
+|---------|---------|-------------|-------------|
+| v0 | 201 seeds, 4 passes | 58% | 64% |
+| v1 | 398 seeds, 4 passes | 60% | 62% |
+| **v3** | **559 seeds, 5 passes** | **pending** | **pending** |
+---
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+base = "Qwen/Qwen2.5-Coder-7B-Instruct"
+adapter = "mechramc/codek-qwen2.5-coder-7b-lora-v3"
+tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter)
+model.eval()
+messages = [
+    {"role": "system", "content": "You are a Python debugging expert. When shown code with a bug, identify the exact location and nature of the bug. Be precise and concise."},
+    {"role": "user", "content": "The following Python code has a subtle bug. Find it.\n\n```python\ndef binary_search(arr, target):\n    lo, hi = 0, len(arr) - 1\n    while lo <= hi:\n        mid = (lo + hi) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            lo = mid\n        else:\n            hi = mid - 1\n    return -1\n```"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=300, do_sample=False)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+---
+## Framework Versions
+- PEFT: 0.18.1
+- TRL: 0.24.0
+- Transformers: 5.5.0
+- PyTorch: 2.6.0
+- Unsloth: 2026.4.1
+- CUDA: 12.4