Update README.md

README.md

@@ -16,12 +16,12 @@ datasets:
 # 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.
+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](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-oasst1-256-6.9b/blob/main/pythia-6.9b.h2ogpt-oig-oasst1-instruct-cleaned-v1.json.1_epochs.5fc91911bc2bfaaf3b6c2de577c4b0ae45a07a4a.9.zip)
+- 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
 
@@ -36,13 +36,13 @@ pip install accelerate==0.18.0
 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")
+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?", 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-oasst1-256-6.9b/blob/main/h2oai_pipeline.py),
+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:
 
 ```python
@@ -90,7 +90,7 @@ GPTNeoXForCausalLM(
 
 ```json
 GPTNeoXConfig {
-  "_name_or_path": "h2oai/h2ogpt-oig-oasst1-256-6.9b",
+  "_name_or_path": "h2oai/h2ogpt-oig-oasst1-256-6_9b",
   "architectures": [
     "GPTNeoXForCausalLM"
   ],