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h2o-llmstudio
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  1. README.md +192 -0
  2. cfg.yaml +93 -0
  3. config.json +31 -0
  4. generation_config.json +7 -0
  5. gitattributes.txt +34 -0
  6. h2oai_pipeline.py +42 -0
  7. pytorch_model-00001-of-00002.bin +3 -0
  8. pytorch_model-00002-of-00002.bin +3 -0
  9. pytorch_model.bin.index.json +330 -0
  10. special_tokens_map.json +14 -0
  11. tokenizer.model +3 -0
  12. tokenizer_config.json +34 -0
README.md ADDED
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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: >-
12
+ https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
13
+ license: apache-2.0
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+ datasets:
15
+ - OpenAssistant/oasst1
16
+ ---
17
+ # Model Card
18
+ ## Summary
19
+
20
+ This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
21
+ - Base model: [openlm-research/open_llama_7b_400bt_preview](https://huggingface.co/openlm-research/open_llama_7b_400bt_preview)
22
+ - Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28)
23
+
24
+
25
+ ## Usage
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+
27
+ 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.
28
+
29
+ ```bash
30
+ pip install transformers==4.28.1
31
+ pip install accelerate==0.18.0
32
+ pip install torch==2.0.0
33
+ ```
34
+
35
+ ```python
36
+ import torch
37
+ from transformers import pipeline
38
+
39
+ generate_text = pipeline(
40
+ model="h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt",
41
+ torch_dtype=torch.float16,
42
+ trust_remote_code=True,
43
+ use_fast=False,
44
+ device_map={"": "cuda:0"},
45
+ )
46
+
47
+ res = generate_text(
48
+ "Why is drinking water so healthy?",
49
+ min_new_tokens=2,
50
+ max_new_tokens=512,
51
+ do_sample=False,
52
+ num_beams=1,
53
+ temperature=float(0.3),
54
+ repetition_penalty=float(1.2),
55
+ renormalize_logits=True
56
+ )
57
+ print(res[0]["generated_text"])
58
+ ```
59
+
60
+ You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
61
+
62
+ ```python
63
+ print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
64
+ ```
65
+
66
+ ```bash
67
+ <|prompt|>Why is drinking water so healthy?</s><|answer|>
68
+ ```
69
+
70
+ Alternatively, if you prefer to not use `trust_remote_code=True` 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:
71
+
72
+
73
+ ```python
74
+ import torch
75
+ from h2oai_pipeline import H2OTextGenerationPipeline
76
+ from transformers import AutoModelForCausalLM, AutoTokenizer
77
+
78
+ tokenizer = AutoTokenizer.from_pretrained(
79
+ "h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt",
80
+ use_fast=False,
81
+ padding_side="left"
82
+ )
83
+ model = AutoModelForCausalLM.from_pretrained(
84
+ "h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt",
85
+ torch_dtype=torch.float16,
86
+ device_map={"": "cuda:0"}
87
+ )
88
+ generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
89
+
90
+ res = generate_text(
91
+ "Why is drinking water so healthy?",
92
+ min_new_tokens=2,
93
+ max_new_tokens=512,
94
+ do_sample=False,
95
+ num_beams=1,
96
+ temperature=float(0.3),
97
+ repetition_penalty=float(1.2),
98
+ renormalize_logits=True
99
+ )
100
+ print(res[0]["generated_text"])
101
+ ```
102
+
103
+
104
+ You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
105
+
106
+ ```python
107
+ from transformers import AutoModelForCausalLM, AutoTokenizer
108
+
109
+ model_name = "h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt" # either local folder or huggingface model name
110
+ # Important: The prompt needs to be in the same format the model was trained with.
111
+ # You can find an example prompt in the experiment logs.
112
+ prompt = "<|prompt|>How are you?</s><|answer|>"
113
+
114
+ tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
115
+ model = AutoModelForCausalLM.from_pretrained(model_name)
116
+ model.cuda().eval()
117
+ inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
118
+
119
+ # generate configuration can be modified to your needs
120
+ tokens = model.generate(
121
+ **inputs,
122
+ min_new_tokens=2,
123
+ max_new_tokens=512,
124
+ do_sample=False,
125
+ num_beams=1,
126
+ temperature=float(0.3),
127
+ repetition_penalty=float(1.2),
128
+ renormalize_logits=True
129
+ )[0]
130
+
131
+ tokens = tokens[inputs["input_ids"].shape[1]:]
132
+ answer = tokenizer.decode(tokens, skip_special_tokens=True)
133
+ print(answer)
134
+ ```
135
+
136
+ ## Model Architecture
137
+
138
+ ```
139
+ LlamaForCausalLM(
140
+ (model): LlamaModel(
141
+ (embed_tokens): Embedding(32000, 4096, padding_idx=0)
142
+ (layers): ModuleList(
143
+ (0-31): 32 x LlamaDecoderLayer(
144
+ (self_attn): LlamaAttention(
145
+ (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
146
+ (k_proj): Linear(in_features=4096, out_features=4096, bias=False)
147
+ (v_proj): Linear(in_features=4096, out_features=4096, bias=False)
148
+ (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
149
+ (rotary_emb): LlamaRotaryEmbedding()
150
+ )
151
+ (mlp): LlamaMLP(
152
+ (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
153
+ (down_proj): Linear(in_features=11008, out_features=4096, bias=False)
154
+ (up_proj): Linear(in_features=4096, out_features=11008, bias=False)
155
+ (act_fn): SiLUActivation()
156
+ )
157
+ (input_layernorm): LlamaRMSNorm()
158
+ (post_attention_layernorm): LlamaRMSNorm()
159
+ )
160
+ )
161
+ (norm): LlamaRMSNorm()
162
+ )
163
+ (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
164
+ )
165
+ ```
166
+
167
+ ## Model Configuration
168
+
169
+ 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.
170
+
171
+
172
+ ## Model Validation
173
+
174
+ Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
175
+
176
+ ```bash
177
+ CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
178
+ ```
179
+
180
+
181
+ ## Disclaimer
182
+
183
+ 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.
184
+
185
+ - 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.
186
+ - 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.
187
+ - 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.
188
+ - 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.
189
+ - 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.
190
+ - 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.
191
+
192
+ 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.
cfg.yaml ADDED
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1
+ architecture:
2
+ backbone_dtype: float16
3
+ force_embedding_gradients: false
4
+ gradient_checkpointing: true
5
+ intermediate_dropout: 0.0
6
+ pretrained: true
7
+ pretrained_weights: ''
8
+ augmentation:
9
+ random_parent_probability: 0.0
10
+ skip_parent_probability: 0.0
11
+ token_mask_probability: 0.0
12
+ dataset:
13
+ add_eos_token_to_answer: true
14
+ add_eos_token_to_prompt: true
15
+ answer_column: output
16
+ data_sample: 1.0
17
+ data_sample_choice:
18
+ - Train
19
+ - Validation
20
+ mask_prompt_labels: true
21
+ parent_id_column: parent_id
22
+ prompt_column:
23
+ - instruction
24
+ text_answer_separator: <|answer|>
25
+ text_prompt_start: <|prompt|>
26
+ train_dataframe: data/user/oasst/train_full_allrank.pq
27
+ validation_dataframe: data/user/oasst/val.csv
28
+ validation_size: 0.01
29
+ validation_strategy: custom
30
+ environment:
31
+ compile_model: false
32
+ find_unused_parameters: false
33
+ gpus:
34
+ - '0'
35
+ - '1'
36
+ - '2'
37
+ mixed_precision: true
38
+ number_of_workers: 8
39
+ seed: -1
40
+ trust_remote_code: false
41
+ use_fsdp: false
42
+ experiment_name: h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt
43
+ llm_backbone: openlm-research/open_llama_7b_400bt_preview
44
+ logging:
45
+ logger: Neptune
46
+ neptune_project: h2o/llm
47
+ number_of_texts: 10
48
+ output_directory: output/user/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt/
49
+ prediction:
50
+ batch_size_inference: 0
51
+ do_sample: false
52
+ max_length_inference: 512
53
+ metric: GPT3.5
54
+ min_length_inference: 2
55
+ num_beams: 1
56
+ num_history: 2
57
+ repetition_penalty: 1.2
58
+ stop_tokens: ''
59
+ temperature: 0.3
60
+ top_k: 0
61
+ top_p: 1.0
62
+ problem_type: text_causal_language_modeling
63
+ tokenizer:
64
+ add_prefix_space: false
65
+ add_prompt_answer_tokens: false
66
+ max_length: 1024
67
+ max_length_answer: 512
68
+ max_length_prompt: 512
69
+ padding_quantile: 1.0
70
+ use_fast: false
71
+ training:
72
+ batch_size: 3
73
+ differential_learning_rate: 1.0e-05
74
+ differential_learning_rate_layers: []
75
+ drop_last_batch: true
76
+ epochs: 2
77
+ evaluate_before_training: false
78
+ evaluation_epochs: 0.5
79
+ grad_accumulation: 1
80
+ gradient_clip: 0.0
81
+ learning_rate: 0.0001
82
+ lora: true
83
+ lora_alpha: 32
84
+ lora_dropout: 0.05
85
+ lora_r: 16
86
+ lora_target_modules: ''
87
+ loss_function: CrossEntropy
88
+ optimizer: AdamW
89
+ save_best_checkpoint: false
90
+ schedule: Cosine
91
+ train_validation_data: false
92
+ warmup_epochs: 0.0
93
+ weight_decay: 0.0
config.json ADDED
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1
+ {
2
+ "_name_or_path": "openlm-research/open_llama_7b_400bt_preview",
3
+ "architectures": [
4
+ "LlamaForCausalLM"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "bos_token_id": 1,
8
+ "custom_pipelines": {
9
+ "text-generation": {
10
+ "impl": "h2oai_pipeline.H2OTextGenerationPipeline",
11
+ "pt": "AutoModelForCausalLM"
12
+ }
13
+ },
14
+ "eos_token_id": 2,
15
+ "hidden_act": "silu",
16
+ "hidden_dropout_prob": 0.0,
17
+ "hidden_size": 4096,
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 11008,
20
+ "max_position_embeddings": 2048,
21
+ "model_type": "llama",
22
+ "num_attention_heads": 32,
23
+ "num_hidden_layers": 32,
24
+ "pad_token_id": 0,
25
+ "rms_norm_eps": 1e-06,
26
+ "tie_word_embeddings": false,
27
+ "torch_dtype": "float16",
28
+ "transformers_version": "4.28.1",
29
+ "use_cache": true,
30
+ "vocab_size": 32000
31
+ }
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.28.1"
7
+ }
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h2oai_pipeline.py ADDED
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1
+ from transformers import TextGenerationPipeline
2
+ from transformers.pipelines.text_generation import ReturnType
3
+
4
+ STYLE = "<|prompt|>{instruction}</s><|answer|>"
5
+
6
+
7
+ class H2OTextGenerationPipeline(TextGenerationPipeline):
8
+ def __init__(self, *args, **kwargs):
9
+ super().__init__(*args, **kwargs)
10
+ self.prompt = STYLE
11
+
12
+ def preprocess(
13
+ self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs
14
+ ):
15
+ prompt_text = self.prompt.format(instruction=prompt_text)
16
+ return super().preprocess(
17
+ prompt_text,
18
+ prefix=prefix,
19
+ handle_long_generation=handle_long_generation,
20
+ **generate_kwargs,
21
+ )
22
+
23
+ def postprocess(
24
+ self,
25
+ model_outputs,
26
+ return_type=ReturnType.FULL_TEXT,
27
+ clean_up_tokenization_spaces=True,
28
+ ):
29
+ records = super().postprocess(
30
+ model_outputs,
31
+ return_type=return_type,
32
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
33
+ )
34
+ for rec in records:
35
+ rec["generated_text"] = (
36
+ rec["generated_text"]
37
+ .split("<|answer|>")[1]
38
+ .strip()
39
+ .split("<|prompt|>")[0]
40
+ .strip()
41
+ )
42
+ return records
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