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---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
<!--
# Model Card for Model ID
-->
## Model Details
<!---->
$$ \begin{align*}
W_{mistral} + LoRA_{zephyr} & = W_{zephyr} \\
W_{zephyr} - LoRA_{zephyr} & = W_{mistral}
\end{align*} $$
<!--
```
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]
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- **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
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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
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.
```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]
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## 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]
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## Environmental Impact
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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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#### Hardware
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#### Software
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**BibTeX:**
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
## 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
--> |