ReasoningwithRicky-7B v0.1-experimental
⚠️ This is an early-stage experimental checkpoint, not a production-ready model.
What this is
A LoRA adapter fine-tuned on Qwen/Qwen3-8B, trained entirely on a free-tier Google Colab T4 GPU as a proof-of-concept, across two sessions (resumed from checkpoint after a session interruption).
Honest limitations (please read before using)
- Training data: only 5,005 examples, 1 epoch. Roughly 1.6% of the originally planned dataset size (~307K examples).
- Math data is GSM8K only (NuminaMath-CoT excluded from this run due to time constraints).
- This adapter primarily teaches output format (e.g.
<think>...</think>reasoning traces, concise code answers, clean Hindi responses) rather than substantially new capability. Underlying reasoning ability is still mostly that of base Qwen3-8B. - Free-tier T4 hardware makes training an 8B-parameter model at meaningful scale impractical in a single day — treat this as a pipeline validation / early checkpoint, not a finished model.
- No formal benchmark evaluation (GSM8K accuracy, HumanEval, etc.) has been run yet.
- No safety/toxicity/hallucination testing has been performed.
Training details
| Base model | Qwen/Qwen3-8B (Apache-2.0) |
| Method | QLoRA (4-bit NF4) |
| LoRA rank / alpha / dropout | 16 / 32 / 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training examples | 5,005 (725 math, 2,225 coding, 1,535 reasoning, 520 Hindi) |
| Epochs | 1 |
| Hardware | 1x NVIDIA T4 (free-tier Google Colab) |
| Context length | 1,024 tokens |
Dataset sources
- Math: openai/gsm8k (MIT)
- Coding: m-a-p/CodeFeedback-Filtered-Instruction (Apache-2.0)
- Reasoning: Open-Orca/OpenOrca (MIT) — derived from GPT-4/GPT-3.5 completions, see dataset card for provenance
- Hindi: soketlabs/bhasha-sft (aya_translated_flan_cot subset)
Intended use
Research and experimentation only — not recommended for production or high-stakes use.
Roadmap
Future versions aim to scale to the full ~307K example dataset with proper category balancing, multi-epoch training on more capable hardware, and full benchmark evaluation before any stable release.
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