125 lines
4.2 KiB
Markdown
125 lines
4.2 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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inference: false
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thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
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tags:
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- gpt
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- llm
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- large language model
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- open-source
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datasets:
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- h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1
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---
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# h2oGPT Model Card
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## Summary
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H2O.ai's `h2ogpt-oig-oasst1-256-6_9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use.
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- Base model: [EleutherAI/pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b)
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- Fine-tuning dataset: [h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1](https://huggingface.co/datasets/h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1)
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- Data-prep and fine-tuning code: [H2O.ai Github](https://github.com/h2oai/h2ogpt)
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- Training logs: [zip](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-256-6_9b/blob/main/pythia-6.9b.h2ogpt-oig-oasst1-instruct-cleaned-v1.json.1_epochs.5fc91911bc2bfaaf3b6c2de577c4b0ae45a07a4a.9.zip)
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## Usage
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To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.
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```bash
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pip install transformers==4.28.1
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pip install accelerate==0.18.0
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```
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```python
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import torch
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from transformers import pipeline
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generate_text = pipeline(model="h2oai/h2ogpt-oig-oasst1-256-6_9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", prompt_type='human_bot')
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res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
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print(res[0]["generated_text"])
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```
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Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-256-6_9b/blob/main/h2oai_pipeline.py),
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store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
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```python
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import torch
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from h2oai_pipeline import H2OTextGenerationPipeline
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6_9b", padding_side="left")
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model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6_9b", torch_dtype=torch.bfloat16, device_map="auto")
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generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer, prompt_type='human_bot')
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res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
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print(res[0]["generated_text"])
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```
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## Model Architecture
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```
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GPTNeoXForCausalLM(
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(gpt_neox): GPTNeoXModel(
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(embed_in): Embedding(50432, 4096)
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(layers): ModuleList(
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(0-31): 32 x GPTNeoXLayer(
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(input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
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(post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
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(attention): GPTNeoXAttention(
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(rotary_emb): RotaryEmbedding()
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(query_key_value): Linear(in_features=4096, out_features=12288, bias=True)
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(dense): Linear(in_features=4096, out_features=4096, bias=True)
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)
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(mlp): GPTNeoXMLP(
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(dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True)
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(dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True)
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(act): GELUActivation()
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)
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)
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)
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(final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
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)
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(embed_out): Linear(in_features=4096, out_features=50432, bias=False)
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)
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```
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## Model Configuration
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```json
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GPTNeoXConfig {
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"_name_or_path": "h2oai/h2ogpt-oig-oasst1-256-6_9b",
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"architectures": [
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"GPTNeoXForCausalLM"
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],
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"bos_token_id": 0,
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"custom_pipelines": {
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"text-generation": {
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"impl": "h2oai_pipeline.H2OTextGenerationPipeline",
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"pt": "AutoModelForCausalLM"
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}
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},
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"eos_token_id": 0,
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"hidden_act": "gelu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neox",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.28.1",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50432
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}
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```
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