Update README.md
Browse files
README.md
CHANGED
@@ -1,200 +1,83 @@
|
|
1 |
---
|
|
|
2 |
license: apache-2.0
|
3 |
-
|
4 |
-
- en
|
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 |
-
### Training Procedure
|
86 |
-
|
87 |
-
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
88 |
-
|
89 |
-
#### Preprocessing [optional]
|
90 |
-
|
91 |
-
[More Information Needed]
|
92 |
-
|
93 |
-
|
94 |
-
#### Training Hyperparameters
|
95 |
-
|
96 |
-
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
97 |
-
|
98 |
-
#### Speeds, Sizes, Times [optional]
|
99 |
-
|
100 |
-
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
101 |
-
|
102 |
-
[More Information Needed]
|
103 |
-
|
104 |
-
## Evaluation
|
105 |
-
|
106 |
-
<!-- This section describes the evaluation protocols and provides the results. -->
|
107 |
-
|
108 |
-
### Testing Data, Factors & Metrics
|
109 |
-
|
110 |
-
#### Testing Data
|
111 |
-
|
112 |
-
<!-- This should link to a Dataset Card if possible. -->
|
113 |
-
|
114 |
-
[More Information Needed]
|
115 |
-
|
116 |
-
#### Factors
|
117 |
-
|
118 |
-
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
119 |
-
|
120 |
-
[More Information Needed]
|
121 |
-
|
122 |
-
#### Metrics
|
123 |
-
|
124 |
-
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
125 |
-
|
126 |
-
[More Information Needed]
|
127 |
-
|
128 |
-
### Results
|
129 |
-
|
130 |
-
[More Information Needed]
|
131 |
-
|
132 |
-
#### Summary
|
133 |
-
|
134 |
-
|
135 |
-
|
136 |
-
## Model Examination [optional]
|
137 |
-
|
138 |
-
<!-- Relevant interpretability work for the model goes here -->
|
139 |
-
|
140 |
-
[More Information Needed]
|
141 |
-
|
142 |
-
## Environmental Impact
|
143 |
-
|
144 |
-
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
145 |
-
|
146 |
-
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
147 |
-
|
148 |
-
- **Hardware Type:** [More Information Needed]
|
149 |
-
- **Hours used:** [More Information Needed]
|
150 |
-
- **Cloud Provider:** [More Information Needed]
|
151 |
-
- **Compute Region:** [More Information Needed]
|
152 |
-
- **Carbon Emitted:** [More Information Needed]
|
153 |
-
|
154 |
-
## Technical Specifications [optional]
|
155 |
-
|
156 |
-
### Model Architecture and Objective
|
157 |
-
|
158 |
-
[More Information Needed]
|
159 |
-
|
160 |
-
### Compute Infrastructure
|
161 |
-
|
162 |
-
[More Information Needed]
|
163 |
-
|
164 |
-
#### Hardware
|
165 |
-
|
166 |
-
[More Information Needed]
|
167 |
-
|
168 |
-
#### Software
|
169 |
-
|
170 |
-
[More Information Needed]
|
171 |
-
|
172 |
-
## Citation [optional]
|
173 |
-
|
174 |
-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
175 |
-
|
176 |
-
**BibTeX:**
|
177 |
-
|
178 |
-
[More Information Needed]
|
179 |
-
|
180 |
-
**APA:**
|
181 |
-
|
182 |
-
[More Information Needed]
|
183 |
-
|
184 |
-
## Glossary [optional]
|
185 |
-
|
186 |
-
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
187 |
-
|
188 |
-
[More Information Needed]
|
189 |
-
|
190 |
-
## More Information [optional]
|
191 |
-
|
192 |
-
[More Information Needed]
|
193 |
-
|
194 |
-
## Model Card Authors [optional]
|
195 |
-
|
196 |
-
[More Information Needed]
|
197 |
-
|
198 |
-
## Model Card Contact
|
199 |
-
|
200 |
-
[More Information Needed]
|
|
|
1 |
---
|
2 |
+
library_name: transformers
|
3 |
license: apache-2.0
|
4 |
+
basemodel: google/gemma-7b
|
|
|
5 |
---
|
6 |
|
7 |
+
## Model Card for Firefly-Gemma
|
8 |
+
|
9 |
+
[firefly-gemma-7b](https://huggingface.co/YeungNLP/firefly-gemma-7b) is trained based on [gemma-7b](https://huggingface.co/google/gemma-7b) to act as a helpful and harmless AI assistant.
|
10 |
+
We use [Firefly](https://github.com/yangjianxin1/Firefly) to train the model on **a single V100 GPU** with QLoRA.
|
11 |
+
|
12 |
+
Our model outperforms the official [gemma-7b-it](https://huggingface.co/google/gemma-7b-it), [zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1), [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) and [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
13 |
+
|
14 |
+
<img src="open_llm_leaderboard.png" width="800">
|
15 |
+
|
16 |
+
We advise you to install transformers>=4.38.1.
|
17 |
+
|
18 |
+
## Performance
|
19 |
+
We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance.
|
20 |
+
|
21 |
+
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|
22 |
+
|--------------------------------|--------|--------|-----------|--------|------------|-----------|--------|
|
23 |
+
| **firefly-gemma-7b** | 62.93 | 62.12 | 79.77 | 61.57 | 49.41 | 75.45 | 49.28 |
|
24 |
+
| zephyr-7b-gemma-v0.1 |62.41|58.45|83.48|60.68|52.07| 74.19| 45.56|
|
25 |
+
| firefly-qwen1.5-en-7b-dpo-v0.1 | 62.36 | 54.35 | 76.04 | 61.21 | 56.4 | 72.06 | 54.13 |
|
26 |
+
| zephyr-7b-beta | 61.95 | 62.03 | 84.36 | 61.07 | 57.45 | 77.74 | 29.04 |
|
27 |
+
| firefly-qwen1.5-en-7b | 61.44 | 53.41 | 75.51 | 61.67 |51.96 |70.72 | 55.34 |
|
28 |
+
| vicuna-13b-v1.5 | 55.41 | 57.08 | 81.24 | 56.67 | 51.51 | 74.66 | 11.3 |
|
29 |
+
| Xwin-LM-13B-V0.1 | 55.29 | 62.54 | 82.8 | 56.53 | 45.96 | 74.27 | 9.63 |
|
30 |
+
| Qwen1.5-7B-Chat | 55.15 | 55.89 | 78.56 | 61.65 | 53.54 | 67.72 | 13.57 |
|
31 |
+
| gemma-7b-it | 53.56 | 51.45 | 71.96 | 53.52 | 47.29 | 67.96 | 29.19 |
|
32 |
+
|
33 |
+
|
34 |
+
|
35 |
+
## Usage
|
36 |
+
The chat template of our chat models is similar as Official gemma-7b-it:
|
37 |
+
```text
|
38 |
+
<bos><start_of_turn>user
|
39 |
+
hello, who are you?<end_of_turn>
|
40 |
+
<start_of_turn>model
|
41 |
+
I am a AI program developed by Firefly<eos>
|
42 |
+
```
|
43 |
+
|
44 |
+
You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py).
|
45 |
+
|
46 |
+
You can also use the following code:
|
47 |
+
```python
|
48 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
49 |
+
import torch
|
50 |
+
|
51 |
+
model_name_or_path = "YeungNLP/firefly-gemma-7b"
|
52 |
+
model = AutoModelForCausalLM.from_pretrained(
|
53 |
+
model_name_or_path,
|
54 |
+
trust_remote_code=True,
|
55 |
+
low_cpu_mem_usage=True,
|
56 |
+
torch_dtype=torch.float16,
|
57 |
+
device_map='auto',
|
58 |
+
)
|
59 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
|
60 |
+
|
61 |
+
prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. "
|
62 |
+
text = f"""
|
63 |
+
<bos><start_of_turn>user
|
64 |
+
{prompt}<end_of_turn>
|
65 |
+
<start_of_turn>model
|
66 |
+
""".strip()
|
67 |
+
model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
|
68 |
+
|
69 |
+
generated_ids = model.generate(
|
70 |
+
model_inputs.input_ids,
|
71 |
+
max_new_tokens=1500,
|
72 |
+
top_p = 0.9,
|
73 |
+
temperature = 0.35,
|
74 |
+
repetition_penalty = 1.0,
|
75 |
+
eos_token_id=tokenizer.encode('<|eos>', add_special_tokens=False)
|
76 |
+
)
|
77 |
+
generated_ids = [
|
78 |
+
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
79 |
+
]
|
80 |
+
|
81 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
82 |
+
print(response)
|
83 |
+
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|