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abrar0503
/
h2ogpt-gm

Text Generation
Transformers
PyTorch
English
llama
gpt
llm
large language model
h2o-llmstudio
text-generation-inference
Model card Files Community
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  1. README.md +181 -0
  2. cfg.yaml +91 -0
  3. config.json +31 -0
  4. generation_config.json +7 -0
  5. gitattributes +34 -0
  6. h2oai_pipeline.py +42 -0
  7. pytorch_model.bin.index.json +330 -0
  8. requirements.txt +24 -0
  9. special_tokens_map.json +26 -0
  10. tokenizer.model +3 -0
  11. 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: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
12
+ license: apache-2.0
13
+ datasets:
14
+ - OpenAssistant/oasst1
15
+ ---
16
+ # Model Card
17
+ ## Summary
18
+
19
+ Try our chatbot here: https://gpt-gm.h2o.ai/
20
+
21
+ This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
22
+ - Base model: [openlm-research/open_llama_7b_preview_300bt](https://huggingface.co/openlm-research/open_llama_7b_preview_300bt)
23
+ - Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28)
24
+
25
+ ## Usage
26
+
27
+ To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `torch` libraries installed.
28
+
29
+ ```bash
30
+ pip install transformers==4.28.1
31
+ pip install torch==2.0.0
32
+ ```
33
+
34
+ ```python
35
+ import torch
36
+ from transformers import pipeline
37
+
38
+ generate_text = pipeline(
39
+ model="h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2",
40
+ torch_dtype=torch.float16,
41
+ trust_remote_code=True,
42
+ use_fast=False,
43
+ device_map={"": "cuda:0"},
44
+ )
45
+
46
+ res = generate_text(
47
+ "Why is drinking water so healthy?",
48
+ min_new_tokens=2,
49
+ max_new_tokens=256,
50
+ do_sample=False,
51
+ num_beams=2,
52
+ temperature=float(0.3),
53
+ repetition_penalty=float(1.2),
54
+ renormalize_logits=True
55
+ )
56
+ print(res[0]["generated_text"])
57
+ ```
58
+
59
+ You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
60
+
61
+ ```python
62
+ print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
63
+ ```
64
+
65
+ ```bash
66
+ <|prompt|>Why is drinking water so healthy?</s><|answer|>
67
+ ```
68
+
69
+ 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:
70
+
71
+
72
+ ```python
73
+ import torch
74
+ from h2oai_pipeline import H2OTextGenerationPipeline
75
+ from transformers import AutoModelForCausalLM, AutoTokenizer
76
+
77
+ tokenizer = AutoTokenizer.from_pretrained(
78
+ "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2",
79
+ use_fast=False,
80
+ padding_side="left"
81
+ )
82
+ model = AutoModelForCausalLM.from_pretrained(
83
+ "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2",
84
+ torch_dtype=torch.float16,
85
+ device_map={"": "cuda:0"}
86
+ )
87
+ generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
88
+
89
+ res = generate_text(
90
+ "Why is drinking water so healthy?",
91
+ min_new_tokens=2,
92
+ max_new_tokens=256,
93
+ do_sample=False,
94
+ num_beams=2,
95
+ temperature=float(0.3),
96
+ repetition_penalty=float(1.2),
97
+ renormalize_logits=True
98
+ )
99
+ print(res[0]["generated_text"])
100
+ ```
101
+
102
+
103
+ You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
104
+
105
+ ```python
106
+ from transformers import AutoModelForCausalLM, AutoTokenizer
107
+
108
+ model_name = "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2" # either local folder or huggingface model name
109
+ # Important: The prompt needs to be in the same format the model was trained with.
110
+ # You can find an example prompt in the experiment logs.
111
+ prompt = "<|prompt|>How are you?</s><|answer|>"
112
+
113
+ tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
114
+ model = AutoModelForCausalLM.from_pretrained(model_name)
115
+ model.cuda().eval()
116
+ inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
117
+
118
+ # generate configuration can be modified to your needs
119
+ tokens = model.generate(
120
+ **inputs,
121
+ min_new_tokens=2,
122
+ max_new_tokens=256,
123
+ do_sample=False,
124
+ num_beams=2,
125
+ temperature=float(0.3),
126
+ repetition_penalty=float(1.2),
127
+ renormalize_logits=True
128
+ )[0]
129
+
130
+ tokens = tokens[inputs["input_ids"].shape[1]:]
131
+ answer = tokenizer.decode(tokens, skip_special_tokens=True)
132
+ print(answer)
133
+ ```
134
+
135
+ ## Model Architecture
136
+
137
+ ```
138
+ LlamaForCausalLM(
139
+ (model): LlamaModel(
140
+ (embed_tokens): Embedding(32000, 4096, padding_idx=0)
141
+ (layers): ModuleList(
142
+ (0-31): 32 x LlamaDecoderLayer(
143
+ (self_attn): LlamaAttention(
144
+ (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
145
+ (k_proj): Linear(in_features=4096, out_features=4096, bias=False)
146
+ (v_proj): Linear(in_features=4096, out_features=4096, bias=False)
147
+ (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
148
+ (rotary_emb): LlamaRotaryEmbedding()
149
+ )
150
+ (mlp): LlamaMLP(
151
+ (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
152
+ (down_proj): Linear(in_features=11008, out_features=4096, bias=False)
153
+ (up_proj): Linear(in_features=4096, out_features=11008, bias=False)
154
+ (act_fn): SiLUActivation()
155
+ )
156
+ (input_layernorm): LlamaRMSNorm()
157
+ (post_attention_layernorm): LlamaRMSNorm()
158
+ )
159
+ )
160
+ (norm): LlamaRMSNorm()
161
+ )
162
+ (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
163
+ )
164
+ ```
165
+
166
+ ## Model Configuration
167
+
168
+ 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.
169
+
170
+ ## Disclaimer
171
+
172
+ 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.
173
+
174
+ - 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.
175
+ - 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.
176
+ - 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.
177
+ - 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.
178
+ - 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.
179
+ - 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.
180
+
181
+ 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
+ - '3'
38
+ mixed_precision: true
39
+ number_of_workers: 8
40
+ seed: -1
41
+ trust_remote_code: false
42
+ use_fsdp: false
43
+ experiment_name: h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2
44
+ llm_backbone: h2o-llmstudio/open_llama_7b_preview_300bt_fix
45
+ logging:
46
+ logger: Neptune
47
+ neptune_project: Zoo/h2o-llm
48
+ number_of_texts: 10
49
+ output_directory: output/user/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2/
50
+ prediction:
51
+ batch_size_inference: 0
52
+ do_sample: false
53
+ max_length_inference: 256
54
+ metric: GPT3.5
55
+ min_length_inference: 2
56
+ num_beams: 2
57
+ repetition_penalty: 1.2
58
+ stop_tokens: ''
59
+ temperature: 0.3
60
+ problem_type: text_causal_language_modeling
61
+ tokenizer:
62
+ add_prefix_space: false
63
+ add_prompt_answer_tokens: false
64
+ max_length: 2048
65
+ max_length_answer: 1024
66
+ max_length_prompt: 2048
67
+ padding_quantile: 1.0
68
+ use_fast: false
69
+ training:
70
+ batch_size: 16
71
+ differential_learning_rate: 1.0e-05
72
+ differential_learning_rate_layers: []
73
+ drop_last_batch: true
74
+ epochs: 3
75
+ evaluate_before_training: false
76
+ evaluation_epochs: 0.5
77
+ grad_accumulation: 1
78
+ gradient_clip: 0.0
79
+ learning_rate: 0.001
80
+ lora: true
81
+ lora_alpha: 32
82
+ lora_dropout: 0.05
83
+ lora_r: 16
84
+ lora_target_modules: ''
85
+ loss_function: CrossEntropy
86
+ optimizer: AdamW
87
+ save_best_checkpoint: false
88
+ schedule: Cosine
89
+ train_validation_data: false
90
+ warmup_epochs: 1.0
91
+ weight_decay: 0.0
config.json ADDED
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1
+ {
2
+ "_name_or_path": "h2o-llmstudio/open_llama_7b_preview_300bt_fix",
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
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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
+ }
gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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
pytorch_model.bin.index.json ADDED
@@ -0,0 +1,330 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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