---
library_name: peft
base_model: codellama/CodeLlama-7b-hf
---

# Model Card for Model ID

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## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]

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## Uses

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### Direct Use

```
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

config = PeftConfig.from_pretrained("hynky/codellama-7b-sft-lora-func-names-java-4bit")
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf",
                                             torch_dtype='auto',
                                             device_map='auto',
                                             offload_folder="offload",
                                             offload_state_dict = True)
model = PeftModel.from_pretrained(model, "hynky/codellama-7b-sft-lora-func-names-java-4bit")

def generate_code(sample, max_new_tokens=200):
    batch = tokenizer(sample, return_tensors='pt').to(device)
    
    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=max_new_tokens)
    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)

print(generate_code("public class AddTwoIntegers("))
```

### 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

Use the code below 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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## 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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure


### Framework versions


- PEFT 0.6.0