File size: 4,492 Bytes
441510f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b933ec4
 
 
5ab451b
b933ec4
 
 
 
 
5ab451b
 
 
441510f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
language:
- en
tags:
- Δ
- LoRA
---

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

## Model Details

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


### Why Though?
unfortunately this is not as simple as [typeof/zephyr-7b-beta-lora](https://huggingface.co/typeof/zephyr-7b-beta-lora) 
due to the way in which [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) was trained... 
by adding tokens, the corresponance is not 1-to-1 with [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
as is the case with [typeof/zephyr-7b-beta-lora](https://huggingface.co/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!


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ox7zGoygsJQFFV3rLT4v9.png)
photo courtesy @teknium [OpenHermes-2.5-Mistral-7B](https://huggingface.co/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.

[More Information Needed]

<!--

```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](https://huggingface.co/mistralai/Mistral-7B-v0.1)

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

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