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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# How to Get Started with the Model
To load and use this model with the Transformers library by Hugging Face, follow the steps outlined in the code snippet below. This code demonstrates how to configure the model, load it along with its tokenizer, and perform inference to generate text based on a given prompt.
## Code Format:
```python
from peft import PeftModel, PeftConfig
test_config = PeftConfig.from_pretrained("checkiejan/phi2-marking-test-full")
model_base = AutoModelForCausalLM.from_pretrained(
test_config.base_model_name_or_path,
device_map="auto",
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
),
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(test_config.base_model_name_or_path)
# Add/set tokens same tokens to base model before merging, like we did before starting training
tokenizer.add_tokens(["<|im_start|>", "<PAD>"])
tokenizer.pad_token = "<PAD>"
tokenizer.add_special_tokens(dict(eos_token="<|im_end|>"))
model_base.resize_token_embeddings(
new_num_tokens=len(tokenizer),
pad_to_multiple_of=64) # phi2 default is 64, see configuration_phi.py
model_base.config.eos_token_id = tokenizer.eos_token_id
lora_model = PeftModel.from_pretrained(model_base, "checkiejan/phi2-marking-test-full")
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True)
outputs = lora_model.generate(**inputs)
text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(''.join(text))
```
This code snippet sets up the model and tokenizer, configures the necessary parameters, and demonstrates how to generate text based on a given prompt. Ensure to replace "Your prompt here" with your actual input text.
## 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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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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### Recommendations
<!-- 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.
[More Information Needed]
## 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]
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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]
<!-- 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]
- **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]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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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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