README.md

---
license: apache-2.0
language:
- en
library_name: transformers
inference: false
---
# h2oGPT Model Card
## Summary

H2O.ai's `h2ogpt-oig-oasst1-256-6.9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use.

- Base model: EleutherAI/pythia-6.9b
- Fine-tuning dataset: [h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1](https://huggingface.co/datasets/h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1)
- Data-prep and fine-tuning code: [H2O.ai Github](https://github.com/h2oai/h2ogpt)
- 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)

## Usage

To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.

```bash
pip install transformers==4.28.1
```

```python
import torch
from transformers import pipeline

generate_text = pipeline(model="h2oai/h2ogpt-oig-oasst1-256-6.9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
res = generate_text("Why is drinking water so healthy?")
print(res[0]["generated_text"])
```

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),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:

```
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6.9b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6.9b", device_map="auto", torch_dtype=torch.bfloat16)

generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
```

### LangChain Usage

To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned 
and the default for the pipeline is to only return the new text.

```
import torch
from transformers import pipeline

generate_text = pipeline(model="h2oai/h2ogpt-oig-oasst1-256-6.9b", torch_dtype=torch.bfloat16,
                         trust_remote_code=True, device_map="auto", return_full_text=True)
```

You can create a prompt that either has only an instruction or has an instruction with context:

```
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline

# template for an instrution with no input
prompt = PromptTemplate(
    input_variables=["instruction"],
    template="{instruction}")

# template for an instruction with input
prompt_with_context = PromptTemplate(
    input_variables=["instruction", "context"],
    template="{instruction}\n\nInput:\n{context}")

hf_pipeline = HuggingFacePipeline(pipeline=generate_text)

llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
```

Example predicting using a simple instruction:

```
print(llm_chain.predict(instruction="Why is drinking water so healthy?").lstrip())
```

Example predicting using an instruction with context:

```
context = """Model A: AUC=0.8
Model from Driverless AI: AUC=0.95
Model C: AUC=0.6
Model D: AUC=0.7
"""

print(llm_context_chain.predict(instruction="Which model performs best?", context=context).lstrip())
```

## Model Architecture

```
GPTNeoXForCausalLM(
  (gpt_neox): GPTNeoXModel(
    (embed_in): Embedding(50432, 4096)
    (layers): ModuleList(
      (0-31): 32 x GPTNeoXLayer(
        (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
        (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
        (attention): GPTNeoXAttention(
          (rotary_emb): RotaryEmbedding()
          (query_key_value): Linear(in_features=4096, out_features=12288, bias=True)
          (dense): Linear(in_features=4096, out_features=4096, bias=True)
        )
        (mlp): GPTNeoXMLP(
          (dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True)
          (dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True)
          (act): GELUActivation()
        )
      )
    )
    (final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
  )
  (embed_out): Linear(in_features=4096, out_features=50432, bias=False)
)
```

## Model Configuration

```
GPTNeoXConfig {
  "_name_or_path": "h2oai/h2ogpt-oig-oasst1-256-6.9b",
  "architectures": [
    "GPTNeoXForCausalLM"
  ],
  "bos_token_id": 0,
  "custom_pipelines": {
    "text-generation": {
      "impl": "h2oai_pipeline.H2OTextGenerationPipeline",
      "pt": "AutoModelForCausalLM"
    }
  },
  "eos_token_id": 0,
  "hidden_act": "gelu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 16384,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 2048,
  "model_type": "gpt_neox",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "rotary_emb_base": 10000,
  "rotary_pct": 0.25,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.28.1",
  "use_cache": true,
  "use_parallel_residual": true,
  "vocab_size": 50432
}

```