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Model: Luckylobster/wisdom_ai
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2026-08-01 03:59:18 +08:00
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---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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"philosophy_and_society:status quo bias change resistance",
"mathematics:Bonferroni correction multiple testing",
"algorithms:minimum spanning tree Kruskal Prim",
"algorithms:dynamic programming memoization",
"ml_fundamentals:ROC curves and AUC",
"reasoning_and_thinking:availability heuristic examples",
"systems_design:container orchestration Kubernetes",
"web_and_backend:SQL injection prevention parameterized queries",
"python_advanced:ctypes and cffi foreign functions",
"ml_fundamentals:feature scaling and normalization",
"web_and_backend:database migrations strategies",
"algorithms:edit distance Levenshtein",
"deep_learning:speculative decoding",
"science:quantum tunneling probability",
"web_and_backend:Docker multi-stage builds",
"systems_design:event sourcing pattern",
"reasoning_and_thinking:Occam's razor parsimony principle",
"reasoning_and_thinking:black swan events preparation",
"science:stellar nucleosynthesis how elements form",
"algorithms:binary search implementation and complexity",
"philosophy_and_society:nature vs nurture genetics environment",
"philosophy_and_society:virtue ethics Aristotle eudaimonia",
"python_advanced:virtual environments and dependency management",
"systems_design:feature flags dark launches",
"science:photoelectric effect quantum mechanics birth",
"reasoning_and_thinking:Bayesian reasoning belief updating",
"philosophy_and_society:moral circle expansion ethics",
"reasoning_and_thinking:network effects Metcalfe's law",
"algorithms:quicksort pivot selection strategies",
"reasoning_and_thinking:second order thinking consequences",
"deep_learning:flash attention optimization",
"reasoning_and_thinking:base rate neglect cognitive bias",
"science:cancer oncogenes tumor suppressors",
"systems_design:blue green deployments",
"reasoning_and_thinking:confirmation bias and debiasing",
"web_and_backend:JWT structure and validation",
"python_advanced:context managers __enter__ __exit__",
"web_and_backend:CORS preflight requests",
"ml_fundamentals:softmax function",
"python_advanced:Python datamodel dunder methods",
"web_and_backend:API documentation OpenAPI",
"systems_design:columnar storage OLAP vs row OLTP",
"deep_learning:recurrent neural networks BPTT",
"reasoning_and_thinking:first principles thinking vs analogy",
"python_advanced:pathlib file operations",
"python_advanced:Python profiling cProfile",
"systems_design:service discovery DNS vs sidecar",
"deep_learning:temperature and top-p sampling",
"deep_learning:GRU vs LSTM comparison",
"mathematics:graph Laplacian spectral theory"
],
"total": 260
}

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{
"model": "Qwen2.5-1.5B",
"trained_on": 30,
"sources": [
"groq",
"gemini"
],
"domains": [
"science",
"math",
"webdev",
"coding",
"general"
],
"lora_rank": 16,
"epochs": 3
}