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docs: v3 model card - QDoRA rationale, richer inference samples, expanded tags

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  1. README.md +5 -5
README.md CHANGED
@@ -30,7 +30,7 @@ language:
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  library_name: peft
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  pipeline_tag: text-generation
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  datasets:
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- - AmareshHebbar/leetcode-code-gen-datasets
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  co2_eq_emissions:
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  emissions: 0
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  source: "estimate, not measured with a carbon-tracking tool"
@@ -48,7 +48,7 @@ model-index:
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  ### Qwen2.5-Coder-7B, QDoRA fine-tuned to solve LeetCode problems in Java
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  [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Model-leetcode--java--qwen25--coder--7b-FFD21E)](https://huggingface.co/AmareshHebbar/leetcode-java-qwen25-coder-7b)
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- [![Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-leetcode--code--gen--datasets-blue)](https://huggingface.co/datasets/AmareshHebbar/leetcode-code-gen-datasets)
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  [![GGUF](https://img.shields.io/badge/GGUF-quantized-6f42c1)](https://huggingface.co/AmareshHebbar/leetcode-java-qwen25-coder-7b-GGUF)
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  [![License](https://img.shields.io/badge/license-Apache%202.0-green)](https://www.apache.org/licenses/LICENSE-2.0)
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  [![Base Model](https://img.shields.io/badge/base-Qwen2.5--Coder--7B-orange)](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct)
@@ -89,7 +89,7 @@ class Solution {
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  |---|---|
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  | **Base model** | [unsloth/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct) |
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  | **Method** | QDoRA (quantized DoRA, not plain LoRA) |
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- | **Training data** | [leetcode-code-gen-datasets](https://huggingface.co/datasets/AmareshHebbar/leetcode-code-gen-datasets) config `java` |
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  | **Data provenance** | scraped from [doocs/leetcode](https://github.com/doocs/leetcode) (3,977 problems), execution-verified, no synthetic/LLM-generated solutions |
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  | **Data quality** | execution-checked against sample I/O (see dataset card for exact rate) |
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  | **Weights here** | QDoRA adapter only (~160MB) β€” load on top of the base model |
@@ -134,7 +134,7 @@ Run `benchmark_suite.py` from the deployment kit to reproduce. All numbers are p
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  |---|---|---|---|---|
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  | [HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x) | Java | _run benchmark_suite.py_ | _run benchmark_suite.py_ | 164 problems, execution-verified |
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  | [MultiPL-E](https://huggingface.co/datasets/nuprl/MultiPL-E) (HumanEval subset) | Java | _run benchmark_suite.py_ | β€” | cross-check vs HumanEval-X |
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- | Held-out LeetCode test split | Java | _run benchmark_suite.py_ | β€” | from `leetcode-code-gen-datasets` (`java`) test split, exact I/O match |
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  | Tokens/sec (fp16, A40) | Java | β€” | β€” | latency benchmark, see script |
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  | Tokens/sec (GGUF q4_k_m, CPU) | Java | β€” | β€” | latency benchmark, see script |
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@@ -384,7 +384,7 @@ one-to-many problem-to-solution structure rather than picking a single "canonica
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  | `build_language_datasets.py` | splits into per-language configs and writes the final train/val/test SFT rows |
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  execution-checked against sample I/O (see dataset card for exact rate). Full extraction/verification/build code lives alongside the
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- [leetcode-code-gen-datasets](https://huggingface.co/datasets/AmareshHebbar/leetcode-code-gen-datasets) dataset card.
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  ### Hyperparameters
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  library_name: peft
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  pipeline_tag: text-generation
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  datasets:
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+ - AmareshHebbar/leetcode-codegen-java
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  co2_eq_emissions:
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  emissions: 0
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  source: "estimate, not measured with a carbon-tracking tool"
 
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  ### Qwen2.5-Coder-7B, QDoRA fine-tuned to solve LeetCode problems in Java
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  [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Model-leetcode--java--qwen25--coder--7b-FFD21E)](https://huggingface.co/AmareshHebbar/leetcode-java-qwen25-coder-7b)
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+ [![Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-leetcode--codegen--java-blue)](https://huggingface.co/datasets/AmareshHebbar/leetcode-codegen-java)
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  [![GGUF](https://img.shields.io/badge/GGUF-quantized-6f42c1)](https://huggingface.co/AmareshHebbar/leetcode-java-qwen25-coder-7b-GGUF)
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  [![License](https://img.shields.io/badge/license-Apache%202.0-green)](https://www.apache.org/licenses/LICENSE-2.0)
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  [![Base Model](https://img.shields.io/badge/base-Qwen2.5--Coder--7B-orange)](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct)
 
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  |---|---|
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  | **Base model** | [unsloth/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct) |
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  | **Method** | QDoRA (quantized DoRA, not plain LoRA) |
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+ | **Training data** | [leetcode-codegen-java](https://huggingface.co/datasets/AmareshHebbar/leetcode-codegen-java) |
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  | **Data provenance** | scraped from [doocs/leetcode](https://github.com/doocs/leetcode) (3,977 problems), execution-verified, no synthetic/LLM-generated solutions |
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  | **Data quality** | execution-checked against sample I/O (see dataset card for exact rate) |
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  | **Weights here** | QDoRA adapter only (~160MB) β€” load on top of the base model |
 
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  |---|---|---|---|---|
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  | [HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x) | Java | _run benchmark_suite.py_ | _run benchmark_suite.py_ | 164 problems, execution-verified |
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  | [MultiPL-E](https://huggingface.co/datasets/nuprl/MultiPL-E) (HumanEval subset) | Java | _run benchmark_suite.py_ | β€” | cross-check vs HumanEval-X |
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+ | Held-out LeetCode test split | Java | _run benchmark_suite.py_ | β€” | from `leetcode-codegen-java` test split, exact I/O match |
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  | Tokens/sec (fp16, A40) | Java | β€” | β€” | latency benchmark, see script |
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  | Tokens/sec (GGUF q4_k_m, CPU) | Java | β€” | β€” | latency benchmark, see script |
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  | `build_language_datasets.py` | splits into per-language configs and writes the final train/val/test SFT rows |
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  execution-checked against sample I/O (see dataset card for exact rate). Full extraction/verification/build code lives alongside the
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+ [leetcode-codegen-java](https://huggingface.co/datasets/AmareshHebbar/leetcode-codegen-java) dataset card.
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  ### Hyperparameters
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