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| # 🚀 Palster Labs | |
| **Palster Labs** is an open AI research and engineering initiative focused on fine-tuning, benchmarking, and openly evaluating modern machine learning models. We operate with a strong emphasis on reproducibility, transparency, and measurable performance improvements. Our primary objective is to bridge the gap between raw pretrained foundation models and domain-specific, production-ready systems. | |
| 🔗 Hugging Face Space: https://huggingface.co/spaces/plasterlabs/ | |
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| ## 🧠 Vision | |
| The rapid evolution of open-weight models has created unprecedented opportunities for independent labs and developers. However, raw pretrained checkpoints are rarely optimized for real-world deployment. Palster Labs exists to: | |
| - Systematically fine-tune open models for specific tasks and domains | |
| - Benchmark models under controlled, reproducible conditions | |
| - Compare architectures and training strategies objectively | |
| - Share findings openly to accelerate collective progress | |
| We treat model development as an engineering discipline: measurable inputs, controlled experiments, and documented outputs. | |
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| ## 🔬 Core Capabilities | |
| ### 1️⃣ Model Fine-Tuning | |
| We specialize in adapting large pretrained models to specialized tasks using modern parameter-efficient and full-fine-tuning strategies. | |
| Our workflow typically includes: | |
| - Dataset curation and preprocessing | |
| - Tokenization strategy optimization | |
| - Hyperparameter search and training stabilization | |
| - Mixed-precision and GPU-optimized training | |
| - Checkpoint validation and ablation testing | |
| We experiment across language, code, reasoning, and multimodal domains. The focus is not only performance gains, but training stability, cost efficiency, and inference scalability. | |
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| ### 2️⃣ Benchmarking & Evaluation | |
| Fine-tuning without rigorous evaluation is incomplete. Every experiment is paired with structured benchmarking that includes: | |
| - Baseline comparisons | |
| - Accuracy and task-specific metrics | |
| - Robustness testing | |
| - Latency and memory profiling | |
| - Structured error analysis | |
| We document configurations, dataset splits, seeds, and evaluation scripts to ensure reproducibility. Results are reported in consistent formats to allow longitudinal tracking across model versions. | |
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| ### 3️⃣ Open Model Ecosystem | |
| Palster Labs primarily works with open-weight and community-driven model families, including: | |
| - Qwen-based architectures | |
| - DeepSeek models | |
| - LLaMA-style derivatives | |
| - Mistral-inspired variants | |
| - Open multimodal systems | |
| We respect upstream licensing requirements and provide proper attribution when releasing derivative checkpoints. | |
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| ## 🛠 Technical Stack | |
| Our tooling emphasizes flexibility and performance: | |
| **Languages** | |
| - Python (primary ML development) | |
| - C++ | |
| - C | |
| **Frameworks & Libraries** | |
| - PyTorch | |
| - Hugging Face Transformers | |
| - Hugging Face Datasets | |
| - Accelerate / distributed training tools | |
| - Custom evaluation pipelines | |
| **Infrastructure** | |
| - GPU-accelerated environments | |
| - Large VRAM training workflows (80GB class GPUs) | |
| - Mixed precision (FP16/BF16) | |
| - Efficient inference with optimized backends | |
| We design training pipelines to scale from notebook experimentation to high-capacity compute environments. | |
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| ## ⚙️ Engineering Principles | |
| Palster Labs operates with several guiding principles: | |
| ### Reproducibility | |
| Every experiment must be repeatable. Config files, dataset references, and environment specifications are clearly defined. | |
| ### Measured Progress | |
| Improvements must be quantified. Claims are validated through controlled comparisons against baselines. | |
| ### Efficiency | |
| Training and inference cost matter. We prioritize parameter-efficient fine-tuning techniques and optimized serving stacks when appropriate. | |
| ### Open Science | |
| Where possible, we publish: | |
| - Benchmark results | |
| - Configuration details | |
| - Model cards | |
| - Evaluation summaries | |
| The goal is knowledge contribution, not opaque performance claims. | |
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| ## 📊 Evaluation Philosophy | |
| We assess models across multiple dimensions: | |
| - Task accuracy and F1 metrics | |
| - Reasoning consistency | |
| - Code generation quality | |
| - Robustness to edge cases | |
| - Resource efficiency (latency / memory usage) | |
| In addition, we experiment with structured validation mechanisms such as: | |
| - Self-verification passes | |
| - Symbolic consistency checks | |
| - Modular validation scripts | |
| - Disagreement-based reruns | |
| Evaluation is treated as an iterative diagnostic process rather than a single final metric. | |
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| ## 🧪 Areas of Focus | |
| Palster Labs actively explores: | |
| - Large Language Model fine-tuning | |
| - Reinforcement learning experimentation | |
| - Competitive agent training | |
| - Lightweight interactive AI applications | |
| - Multimodal reasoning systems | |
| - Benchmark dataset construction | |
| We are particularly interested in bridging research experimentation with deployable engineering systems. | |
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| ## 🚀 Using Our Work | |
| To explore our releases and demos: | |
| 1. Visit the Hugging Face Space linked above. | |
| 2. Review available models and interactive demos. | |
| 3. Examine associated documentation and evaluation results. | |
| 4. Reproduce experiments using published configs where available. | |
| When deploying any released models, always review licensing and intended-use notes in the corresponding model card. | |
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| ## 🤝 Collaboration & Contributions | |
| We welcome collaboration from researchers, engineers, and students. Contribution pathways include: | |
| - Proposing new benchmarks | |
| - Improving evaluation robustness | |
| - Optimizing training pipelines | |
| - Contributing dataset preprocessing tools | |
| - Suggesting reproducibility improvements | |
| When submitting contributions, include clear documentation and reproducible instructions. | |
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| ## 👤 Maintainer | |
| Palster Labs is independently maintained by HIMANSHU KANT CHORISHYA. | |
| For inquiries, collaboration proposals, or technical discussion: | |
| - Use the Hugging Face Space messaging interface | |
| - Open issues in associated repositories | |
| Please include reproducible logs or configuration details when reporting technical concerns. | |
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| ## 📜 Licensing | |
| Code released by Palster Labs typically follows permissive open-source licensing (e.g., MIT or Apache-2.0). | |
| Model checkpoints inherit and respect upstream license constraints. | |
| Datasets are used in accordance with their respective terms of use. | |
| Always review individual project licenses before commercial deployment. | |
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| ## 🔭 Roadmap | |
| Future directions include: | |
| - Expanded structured evaluation dashboards | |
| - Cross-model comparative benchmarks | |
| - Automated experiment tracking | |
| - Improved deployment templates for Hugging Face Spaces | |
| - Scalable distributed training utilities | |
| Our long-term goal is to establish Palster Labs as a transparent, technically rigorous open AI experimentation hub. | |
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| ## 🧰 Tech stack & badges | |
| We use standard ML infra and languages — replace badges with repo-hosted assets if preferred. | |
| | Tech | Badge | | |
| |------|-------| | |
| | Python |  | | |
| | PyTorch |  | | |
| | Hugging Face |  | | |
| | C++ |  | | |
| | C |  | | |
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| # 🧠 Open Model Ecosystem | |
| We experiment with leading open-weight models: | |
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| We fine-tune, evaluate, and compare architectures across reasoning, coding, multimodal, and task-specific workloads. | |
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| # 🛠 Frameworks & Libraries | |
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| We design modular pipelines that scale from notebook prototypes to large GPU clusters. | |
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| # 💻 Programming Languages | |
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| Python is our primary research language, while C/C++ are used for performance-critical systems and inference optimizations. | |
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| # 🖥 Local AI & Deployment Tools | |
| We support and experiment with local inference ecosystems: | |
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| We test models for: | |
| - High-throughput inference | |
| - Memory efficiency | |
| - Quantization performance | |
| - Local deployment stability | |
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| **Palster Labs — Fine-Tune. Benchmark. Openly Improve.** |