zephyr-7b-beta-lora

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Model Details

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$$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} \ W_{zephyr} - LoRA_{zephyr} = W_{mistral} $$

<!-- $$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} $$ ``` 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] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## 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

How to Get Started with the Model

Use the code below to get started with the model.

# 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 [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## 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 -->

Summary

Zephyr-7B-β is a fine-tuned version of mistralai/Mistral-7B-v0.1 Zephyr-7B technical report

LoRA QLoRA