openhermes-2.5-mistral-lora

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Model Details

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

Why Though?

unfortunately this is not as simple as typeof/zephyr-7b-beta-lora due to the way in which OpenHermes-2.5-Mistral-7B was trained... by adding tokens, the corresponance is not 1-to-1 with mistralai/Mistral-7B-v0.1 as is the case with typeof/zephyr-7b-beta-lora ... nevertheless, if you have found yourself here, I'm sure you can figure out how to use it... if not, open up an issue!

photo courtesy @teknium OpenHermes-2.5-Mistral-7B was trained...

<!-- $$ W_{mistral} + LoRA_{zephyr} = W_{zephyr} $$ ``` 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. -->

How to Get Started with the Model

Use the code below to get started with the model.

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<!-- ```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> ``` -->

Summary

A fine-tuned version of mistralai/Mistral-7B-v0.1

LoRA

QLoRA