datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning
PyReason-7k: Advanced Python Chain-of-Thought Dataset
Dataset Description
This dataset contains 7,000+ high-quality Python programming examples designed for LLM fine-tuning.
Each entry includes a detailed thought_process (Chain-of-Thought) to teach models logical reasoning before coding.
Key Features:
Chain-of-Thought: Step-by-step reasoning traces.
Error Handling: Solutions include try-except blocks and logging.
Diverse Tasks: Algorithms, API handling, Data Structures.… See the full description on the dataset page: https://huggingface.co/datasets/xTayyub/High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning.reasoning-sft-poor-quality-reasoning-sample-mix
Reasoning SFT Sample Mix
A mixed-domain reasoning SFT dataset with multiple response variants per input at varying levels of verbosity and style.
Format
Each row contains an input conversation and several response columns representing different generation strategies applied to the same prompt.
Usage
from datasets import load_dataset
ds = load_dataset("AmanPriyanshu/reasoning-sft-poor-quality-reasoning-sample-mix", split="train")
License
Apache 2.0
microsoft-phi-3-5-mini-instruct__llm-quality-reasoning-mini__019e3b5d39b2
microsoft/Phi-3.5-mini-instruct on llm.quality.reasoning-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
10
N Ok
10
Ok Rate
1
Accuracy
0.2
Accuracy P05
0
Accuracy P50
0
Accuracy P95
1
TTFT P50
11.2253
ms
Total P50 Ms
192.4104
Tokens Out Total
286
Run configuration
Model: microsoft/Phi-3.5-mini-instruct @ unknown00
Engine: vllm vunknown
Quantization: fp16
Hardware:… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/microsoft-phi-3-5-mini-instruct__llm-quality-reasoning-mini__019e3b5d39b2.