Practical overview
When it fits
Use it when you want a research-oriented reference pipeline for small-model reasoning experiments, particularly if you are working with AMD ROCm hardware and Hugging Face artifacts.
Who it is for
- Researchers studying training methods for compact language models
- Practitioners building or examining reasoning-dataset curation workflows
- Developers interested in supervised fine-tuning and preference alignment experiments
Documented uses
- Curate task-specific datasets from streamed sources using stage-specific YAML configurations.
- Fine-tune a small SmolLM2-based model first without and then with structured reasoning traces.
- Apply preference alignment to chosen and rejected reasoning completions.
- Inspect the stages and artifacts associated with training and evaluation.
Requirements & limits
The project is described as an open pipeline in a public GitHub repository. Its setup instructions use uv and specify Hugging Face and Weights & Biases credentials before training.
- The target model is a 135-million-parameter SmolLM2-based model.
- The repository's main execution target is AMD ROCm; other setups require adaptation, and no alternative configuration is documented as tested.
- The README describes the current model as a research prototype with limited capabilities and hallucination tendencies, rather than an everyday-use model.
- The data builder supports configurable source streaming, column transformations, provenance fields, and optional Hub publication with a generated dataset card.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the Python environment
Set up the repository environment and the credentials the README specifies before training.
Before you start
- A checkout of the repository
- Python environment managed with uv
- Hugging Face and Weights & Biases credentials before training
Steps
Install uv using the instructions for your operating system.
Run the command below in the repository to create and synchronize the environment.
uv syncActivate the virtual environment if it was not activated automatically.
Before training, run
huggingface-cli loginandwandb login.
Expected outcome
The project environment is synchronized and the documented training credentials are configured.
Guide limitations
- This covers setup only; it does not document a training command or verify a successful run.
- The project is mainly designed for AMD ROCm hardware; adapting it to another setup requires modification.
Build a dataset artifact from a stage configuration
Use the documented collection script to build a stage-one dataset artifact and optionally publish it to Hugging Face Hub.
Before you start
- A synchronized project environment
- The stage YAML configuration at
data/config/stage_1.yaml - Hugging Face credentials if uploading the result
Steps
Run the command below.
uv run data/data_collection.py --config-path data/config/stage_1.yaml --output-dir data/artefacts/stage_1Add
--upload-to-hubwhen you want the shuffled dataset pushed to Hugging Face Hub.
Expected outcome
The collection workflow writes the dataset artifact to the chosen output directory and, when requested, can push it with an automatically generated dataset card.
Guide limitations
- The command shown is for stage one; other stages use their own YAML specifications.
- The documented workflow describes dataset construction, not model training.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- Shekswess/tiny-reasoning-language-model · Official homepage title · retained in its source language