README.md

---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
language:
- en
pipeline_tag: text-generation
widget:
  - text: "How many helicopters can a human eat in one sitting?"
tags:
- Δ
- LoRA
---

<!--
# Model Card for Model ID
-->

## Model Details

<!--![image/png](https://cdn-uploads.huggingface.co/production/uploads/648b0f4fd8fe693f51de98d2/aerBANxBtCya732NdBiw0.png)-->
$$
W_{mistral} + LoRA_{zephyr} = W_{zephyr} \\
W_{zephyr} - LoRA_{zephyr} = W_{mistral}
$$

<!--
$$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} $$
```
typeof/zephyr-7b-beta-lora + mistralai/Mistral-7B-v0.1
= HuggingFaceH4/zephyr-7b-beta
````

### Model Description

- **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]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]


### Model Sources [optional]

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

### Direct Use

[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

[More Information Needed]

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-->

### Model Sources
[HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)

## How to Get Started with the Model

Use the code below to get started with the model.

```python
# pip install transformers peft

import torch
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "typeof/zephyr-7b-beta-lora"

model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)

tokenizer_id = "HuggingFaceH4/zephyr-7b-beta" # for chat template etc...
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
```
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s> 
<|user|>
How many helicopters can a human eat in one sitting?</s> 
<|assistant|> 
Well, me matey, that’s a good question indeed! I’ve never seen 
a human eat a helicopter, and I don’t think many others have 
either. However, I’ve heard rumors that some people have 
eaten entire airplanes, so I suppose it’s not entirely unheard 
of.

As for the number of helicopters one could eat, that depends 
on the size and weight of the helicopter. A small, lightweight 
helicopter would be easier to eat than a large, heavy one. 
In fact, I’ve heard that some people have eaten entire helicopters 
as part of a dare or a challenge.

So, my advice to you, me hearty, is to steer clear of helicopters 
and stick to more traditional fare. Yarr!</s>
```
<!--

## Training Details

### Training Data


[More Information Needed]

### Training Procedure


#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

#### Speeds, Sizes, Times [optional]


[More Information Needed]

## Evaluation


### Testing Data, Factors & Metrics

#### Testing Data


[More Information Needed]

#### Factors


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#### Metrics


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### Results

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#### Summary

## Model Examination [optional]

[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]

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure

The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_4bit: True
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True

### Framework versions

- PEFT 0.6.3.dev0

-->
#### Summary

[Zephyr-7B-β](https://arxiv.org/abs/2305.18290) is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[Zephyr-7B technical report](https://arxiv.org/abs/2310.16944)

[LoRA](https://arxiv.org/abs/2305.14314)
[QLoRA](https://arxiv.org/abs/2106.09685)