---
library_name: transformers
base_model:
- bigcode/starcoder2-7b
---

# Model Card for Model ID

# StarCoder2 Fine-Tuned 

This is a fine-tuned version of the StarCoder2 model on a specific dataset for code generation or a targeted task. The base model is `bigcode/starcoder2-7b`, and this model has been fine-tuned on the `CodeAlpaca_20K` python dataset.




## Model Details
**Base Model:** `bigcode/starcoder2-7b`  
**Fine-Tuned With:** LoRA (Low-Rank Adaptation)  
**Quantization:** 4-bit precision using `bitsandbytes`  
**Fine-Tuning Framework:** Hugging Face `transformers` and `peft`  
**Dataset:** CodeAlpaca_20K (or other dataset)  
**Max Sequence Length:** 1024 tokens  
**Use Case:** Code generation or Q&A for programming-related tasks.  
**Model Size:** 7B parameters  

### Model Description

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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:** Keshav Singh
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Starcoder2-7b

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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

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## Training Details

### Training Data

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### Training Procedure

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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