File size: 4,694 Bytes
7ee2ccc
 
25f6e24
 
 
13d8a16
7b42734
25f6e24
 
 
 
7ee2ccc
7b42734
25f6e24
7b42734
25f6e24
7b42734
25f6e24
dab1c5d
25f6e24
dab1c5d
 
25f6e24
dab1c5d
 
25f6e24
dab1c5d
25f6e24
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2f7732f
 
 
485cc60
2f7732f
6b43e2c
2f7732f
 
 
 
 
 
 
 
 
 
 
25f6e24
2f7732f
 
 
25f6e24
 
 
 
2f7732f
 
25f6e24
2f7732f
 
 
25f6e24
2f7732f
 
 
 
 
 
 
25f6e24
2f7732f
 
25f6e24
2f7732f
25f6e24
 
 
7b42734
4231f7a
7b42734
4231f7a
7b42734
4231f7a
 
 
 
 
 
 
 
 
 
 
 
7b42734
4231f7a
7b42734
4231f7a
 
 
 
25f6e24
 
 
 
 
 
 
 
 
 
 
 
 
13d8a16
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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
---
license: other
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- decapoda-research-7b-hf
- prompt answering
- peft
---

## Model Card for Model ID

This repository contains a LLaMA-7B further fine-tuned model on conversations and question answering prompts.

This model is a fine-tuned version of [chainyo/alpaca-lora-7b](https://huggingface.co/chainyo/alpaca-lora-7b) on conversations dataset.

⚠️ **I used [LLaMA-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) as a base model, so this model is for Research purpose only (See the [license](https://huggingface.co/decapoda-research/llama-7b-hf/blob/main/LICENSE))**


## Model Details


### Model Description

The decapoda-research/llama-7b-hf model was finetuned on conversations and question answering prompts.

**Developed by:** [More Information Needed]

**Shared by:** [More Information Needed]

**Model type:** Causal LM

**Language(s) (NLP):** English, multilingual

**License:** Research

**Finetuned from model:** decapoda-research/llama-7b-hf


## Model Sources [optional]

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

## Uses

The model can be used for prompt answering


### Direct Use

The model can be used for prompt answering


### Downstream Use

Generating text and prompt answering


## 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.


# Usage

## Creating prompt

The model was trained on the following kind of prompt:

```python
def generate_prompt(instruction: str, input_ctxt: str = None) -> str:
    if input_ctxt:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input_ctxt}

### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:"""
```

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering")
model = LlamaForCausalLM.from_pretrained(
    "Sandiago21/llama-7b-hf-prompt-answering",
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)
generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=128,
)

model.eval()
if torch.__version__ >= "2":
    model = torch.compile(model)
```

### Example of Usage
```python
instruction = "What is the capital city of Greece and with which countries does Greece border?"
input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.

prompt = generate_prompt(instruction, input_ctxt)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
input_ids = input_ids.to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
    )

response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
print(response)

>>> The capital city of Greece is Athens and it borders Albania, Macedonia, Bulgaria and Turkey.
```

## Training Details

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.12.1

### Training Data

The decapoda-research/llama-7b-hf was finetuned on conversations and question answering data


### Training Procedure

The decapoda-research/llama-7b-hf model was further trained and finetuned on question answering and prompts data for 1 epoch (approximately 10 hours of training on a single GPU)


## Model Architecture and Objective

The model is based on decapoda-research/llama-7b-hf model and finetuned adapters on top of the main model on conversations and question answering data.