psinger commited on
Commit
8755c39
1 Parent(s): 2229644

Upload model card

Browse files
Files changed (1) hide show
  1. README.md +195 -0
README.md ADDED
@@ -0,0 +1,195 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ library_name: transformers
5
+ tags:
6
+ - gpt
7
+ - llm
8
+ - large language model
9
+ - h2o-llmstudio
10
+ inference: false
11
+ thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
12
+ ---
13
+ # Model Card
14
+ ## Summary
15
+
16
+ This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
17
+ - Base model: [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
18
+
19
+
20
+ ## Usage
21
+
22
+ To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
23
+
24
+ ```bash
25
+ pip install transformers==4.29.0
26
+ pip install accelerate==0.20.3
27
+ pip install torch==2.0.0
28
+ ```
29
+
30
+ ```python
31
+ import torch
32
+ from transformers import pipeline
33
+
34
+ generate_text = pipeline(
35
+ model="psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
36
+ torch_dtype="auto",
37
+ trust_remote_code=True,
38
+ use_fast=False,
39
+ device_map={"": "cuda:0"},
40
+ )
41
+
42
+ res = generate_text(
43
+ "Why is drinking water so healthy?",
44
+ min_new_tokens=2,
45
+ max_new_tokens=1024,
46
+ do_sample=False,
47
+ num_beams=1,
48
+ temperature=float(0.3),
49
+ repetition_penalty=float(1.2),
50
+ renormalize_logits=True
51
+ )
52
+ print(res[0]["generated_text"])
53
+ ```
54
+
55
+ You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
56
+
57
+ ```python
58
+ print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
59
+ ```
60
+
61
+ ```bash
62
+ <|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
63
+ ```
64
+
65
+ Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
66
+
67
+ ```python
68
+ import torch
69
+ from h2oai_pipeline import H2OTextGenerationPipeline
70
+ from transformers import AutoModelForCausalLM, AutoTokenizer
71
+
72
+ tokenizer = AutoTokenizer.from_pretrained(
73
+ "psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
74
+ use_fast=False,
75
+ padding_side="left",
76
+ trust_remote_code=True,
77
+ )
78
+ model = AutoModelForCausalLM.from_pretrained(
79
+ "psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
80
+ torch_dtype="auto",
81
+ device_map={"": "cuda:0"},
82
+ trust_remote_code=True,
83
+ )
84
+ generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
85
+
86
+ res = generate_text(
87
+ "Why is drinking water so healthy?",
88
+ min_new_tokens=2,
89
+ max_new_tokens=1024,
90
+ do_sample=False,
91
+ num_beams=1,
92
+ temperature=float(0.3),
93
+ repetition_penalty=float(1.2),
94
+ renormalize_logits=True
95
+ )
96
+ print(res[0]["generated_text"])
97
+ ```
98
+
99
+
100
+ You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
101
+
102
+ ```python
103
+ from transformers import AutoModelForCausalLM, AutoTokenizer
104
+
105
+ model_name = "psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2" # either local folder or huggingface model name
106
+ # Important: The prompt needs to be in the same format the model was trained with.
107
+ # You can find an example prompt in the experiment logs.
108
+ prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
109
+
110
+ tokenizer = AutoTokenizer.from_pretrained(
111
+ model_name,
112
+ use_fast=False,
113
+ trust_remote_code=True,
114
+ )
115
+ model = AutoModelForCausalLM.from_pretrained(
116
+ model_name,
117
+ torch_dtype="auto",
118
+ device_map={"": "cuda:0"},
119
+ trust_remote_code=True,
120
+ )
121
+ model.cuda().eval()
122
+ inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
123
+
124
+ # generate configuration can be modified to your needs
125
+ tokens = model.generate(
126
+ **inputs,
127
+ min_new_tokens=2,
128
+ max_new_tokens=1024,
129
+ do_sample=False,
130
+ num_beams=1,
131
+ temperature=float(0.3),
132
+ repetition_penalty=float(1.2),
133
+ renormalize_logits=True
134
+ )[0]
135
+
136
+ tokens = tokens[inputs["input_ids"].shape[1]:]
137
+ answer = tokenizer.decode(tokens, skip_special_tokens=True)
138
+ print(answer)
139
+ ```
140
+
141
+ ## Model Architecture
142
+
143
+ ```
144
+ RWForCausalLM(
145
+ (transformer): RWModel(
146
+ (word_embeddings): Embedding(65024, 8192)
147
+ (h): ModuleList(
148
+ (0-59): 60 x DecoderLayer(
149
+ (ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
150
+ (ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
151
+ (self_attention): Attention(
152
+ (maybe_rotary): RotaryEmbedding()
153
+ (query_key_value): Linear(in_features=8192, out_features=9216, bias=False)
154
+ (dense): Linear(in_features=8192, out_features=8192, bias=False)
155
+ (attention_dropout): Dropout(p=0.0, inplace=False)
156
+ )
157
+ (mlp): MLP(
158
+ (dense_h_to_4h): Linear(in_features=8192, out_features=32768, bias=False)
159
+ (act): GELU(approximate='none')
160
+ (dense_4h_to_h): Linear(in_features=32768, out_features=8192, bias=False)
161
+ )
162
+ )
163
+ )
164
+ (ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
165
+ )
166
+ (lm_head): Linear(in_features=8192, out_features=65024, bias=False)
167
+ )
168
+ ```
169
+
170
+ ## Model Configuration
171
+
172
+ This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
173
+
174
+
175
+ ## Model Validation
176
+
177
+ Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
178
+
179
+ ```bash
180
+ CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=psinger/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
181
+ ```
182
+
183
+
184
+ ## Disclaimer
185
+
186
+ Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
187
+
188
+ - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
189
+ - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
190
+ - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
191
+ - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
192
+ - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
193
+ - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
194
+
195
+ By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.