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README.md

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 ---
 license: apache-2.0
+language:
+- en
+library_name: transformers
+inference: false
+thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
+tags:
+- gpt
+- llm
+- large language model
+- open-source
+datasets:
+- h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1
 ---
+# h2oGPT Model Card
+## Summary
+
+H2O.ai's `h2ogpt-oig-oasst1-512-6.9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use.
+
+- Base model: [EleutherAI/pythia-6.9b](https://huggingface.co/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-oasst1-512-6.9b/blob/main/pythia-6.9b.h2ogpt-oig-oasst1-instruct-cleaned-v1.json.1_epochs.5fc91911bc2bfaaf3b6c2de577c4b0ae45a07a4a.7.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
+pip install accelerate==0.18.0
+```
+
+```python
+import torch
+from transformers import pipeline
+
+generate_text = pipeline(model="h2oai/h2ogpt-oig-oasst1-512-6.9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+
+res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
+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-oasst1-512-6.9b/blob/main/h2oai_pipeline.py),
+store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
+
+```python
+import torch
+from h2oai_pipeline import H2OTextGenerationPipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oig-oasst1-512-6.9b", padding_side="left")
+model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oig-oasst1-512-6.9b", torch_dtype=torch.bfloat16, device_map="auto")
+generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
+
+res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
+print(res[0]["generated_text"])
+```
+
+## 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
+
+```json
+GPTNeoXConfig {
+  "_name_or_path": "h2oai/h2ogpt-oig-oasst1-512-6.9b",
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "custom_pipeline": {
+    "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
+}
+
+```