File size: 6,680 Bytes
047dd6d
 
 
ffc2da5
047dd6d
 
3a554d5
 
047dd6d
 
ffc2da5
 
047dd6d
 
 
 
1c07f34
047dd6d
1c07f34
047dd6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c07f34
047dd6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c07f34
 
 
 
 
047dd6d
 
 
 
 
 
 
 
 
 
 
3a554d5
047dd6d
3a554d5
 
 
047dd6d
1c07f34
047dd6d
1c07f34
 
 
 
 
 
 
 
047dd6d
1c07f34
047dd6d
1c07f34
047dd6d
 
1c07f34
047dd6d
 
 
3a554d5
 
 
 
 
 
 
047dd6d
 
 
 
 
 
 
 
1c07f34
047dd6d
1c07f34
047dd6d
 
 
 
 
 
 
 
 
 
 
 
 
 
3a554d5
047dd6d
 
 
 
 
 
 
3a554d5
047dd6d
1c07f34
047dd6d
 
1c07f34
 
 
 
 
 
 
 
 
 
047dd6d
1c07f34
047dd6d
1c07f34
047dd6d
 
1c07f34
047dd6d
 
 
3a554d5
 
 
 
 
 
 
047dd6d
 
 
 
 
 
 
 
 
1c07f34
047dd6d
1c07f34
047dd6d
 
 
 
 
 
 
 
 
 
 
 
 
 
3a554d5
047dd6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
---
language:
- en
license: other
library_name: transformers
tags:
- falcon
- falcon-7b
- prompt answering
- peft
pipeline_tag: text-generation
base_model: tiiuae/falcon-7b
---

## Model Card for Model ID

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

**I used falcon-7b (https://huggingface.co/tiiuae/falcon-7b) as a base model, so this model has the same license with Falcon-7b model (Apache-2.0)**


## Model Details

Anyone can use (ask prompts) and play with the model using the pre-existing Jupyter Notebook in the **noteboooks** folder. The Jupyter Notebook contains example code to load the model and ask prompts to it as well as example prompts to get you started.

### Model Description

The tiiuae/falcon-7b 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:** Apache-2.0

**Finetuned from model:** tiiuae/falcon-7b


## 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(prompt: str) -> str:
    return f"""
    <human>: {prompt}
    <assistant>: 
    """.strip()
```

## How to Get Started with the Model

Use the code below to get started with the model.

1. You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode:

```python
import torch
from peft import PeftConfig, PeftModel
from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

MODEL_NAME = "."

config = PeftConfig.from_pretrained(MODEL_NAME)

compute_dtype = getattr(torch, "float16")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

model = PeftModel.from_pretrained(model, MODEL_NAME)

generation_config = model.generation_config
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.max_new_tokens = 32
generation_config.use_cache = False
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id

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

### Example of Usage
```python
prompt = "What is the capital city of Greece and with which countries does Greece border?"

prompt = generate_prompt(prompt)
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, Bulgaria, Macedonia, and Turkey.
```

2. You can also directly call the model from HuggingFace using the following code snippet:

```python
import torch
from peft import PeftConfig, PeftModel
from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

MODEL_NAME = "Sandiago21/falcon-7b-prompt-answering"
BASE_MODEL = "tiiuae/falcon-7b"

compute_dtype = getattr(torch, "float16")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

model = PeftModel.from_pretrained(model, MODEL_NAME)

generation_config = model.generation_config
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.max_new_tokens = 32
generation_config.use_cache = False
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id

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

### Example of Usage

```python
prompt = "What is the capital city of Greece and with which countries does Greece border?"

prompt = generate_prompt(prompt)
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, Bulgaria, Macedonia, 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 tiiuae/falcon-7b was finetuned on conversations and question answering data

### Training Procedure

The tiiuae/falcon-7b 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 tiiuae/falcon-7b model and finetuned adapters on top of the main model on conversations and question answering data.