Update README.md

Change model name

README.md

@@ -5,20 +5,20 @@ language:
 library_name: transformers
 inference: false
 ---
-# dolly-v2-6-9b Model Card
+# dolly-v2-7b Model Card
 ## Summary
 
-Databricks’ `dolly-v2-6-9b`, an instruction-following large language model trained on the Databricks machine learning platform 
+Databricks’ `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform 
 that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records 
 [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated 
 by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
-information extraction, open QA and summarization. `dolly-v2-6-9b` is not a state-of-the-art model, but does exhibit surprisingly 
+information extraction, open QA and summarization. `dolly-v2-7b` is not a state-of-the-art model, but does exhibit surprisingly 
 high quality instruction following behavior not characteristic of the foundation model on which it is based.  
 
 **Owner**: Databricks, Inc.
 
 ## Model Overview
-`dolly-v2-6-9b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from 
+`dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from 
 [EleutherAI’s](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned 
 on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA)
 
@@ -32,7 +32,7 @@ In a Databricks notebook you could run:
 ```
 
 The instruction following pipeline can be loaded using the `pipeline` function as shown below.  This loads a custom `InstructionTextGenerationPipeline` 
-found in the model repo [here](https://huggingface.co/databricks/dolly-v2-6-9b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
+found in the model repo [here](https://huggingface.co/databricks/dolly-v2-7b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
 Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage.  It does not appear to impact output quality.
 It is also fine to remove it if there is sufficient memory.
 
@@ -40,7 +40,7 @@ It is also fine to remove it if there is sufficient memory.
 import torch
 from transformers import pipeline
 
-generate_text = pipeline(model="databricks/dolly-v2-6-9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
 ```
 
 You can then use the pipeline to answer instructions:
@@ -49,15 +49,15 @@ You can then use the pipeline to answer instructions:
 generate_text("Explain to me the difference between nuclear fission and fusion.")
 ```
 
-Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-6-9b/blob/main/instruct_pipeline.py),
+Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-7b/blob/main/instruct_pipeline.py),
 store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
 
 ```
 from instruct_pipeline import InstructionTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-6-9b", padding_side="left")
-model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-6-9b", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-7b", padding_side="left")
+model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-7b", device_map="auto")
 
 generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
 ```
@@ -66,23 +66,23 @@ generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokeniz
 ## Known Limitations
 
 ### Performance Limitations
-**`dolly-v2-6-9b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform 
+**`dolly-v2-7b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform 
 competitively with more modern model architectures or models subject to larger pretraining corpuses.  
 
 The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community.  
-In particular, `dolly-v2-6-9b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, 
+In particular, `dolly-v2-7b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, 
 dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.
-Moreover, we find that `dolly-v2-6-9b` does not have some capabilities, such as well-formatted letter writing, present in the original model.  
+Moreover, we find that `dolly-v2-7b` does not have some capabilities, such as well-formatted letter writing, present in the original model.  
 
 ### Dataset Limitations
-Like all language models, `dolly-v2-6-9b` reflects the content and limitations of its training corpuses. 
+Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses. 
 
 - **The Pile**: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
 it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
 in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
 associations.
 
-- **`databricks-dolly-15k`**: The training data on which `dolly-v2-6-9b` is instruction tuned represents natural language instructions generated
+- **`databricks-dolly-15k`**: The training data on which `dolly-v2-7b` is instruction tuned represents natural language instructions generated
 by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages
 for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or
 personally identifying information about non-public figures, but it may contain typos and factual errors.
@@ -95,7 +95,7 @@ maximize the potential of all individuals and organizations.
 ### Benchmark Metrics
 
 Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); 
-model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-6-9b` is not state of the art, 
+model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-7b` is not state of the art, 
 and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, 
 but a robust statement as to the sources of these variations requires further study.  
 
@@ -105,11 +105,11 @@ but a robust statement as to the sources of these variations requires further st
 +-----------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------|
 | EleutherAI/pythia-2.8b            |        0.348 |   0.585859 |     0.589582 |    0.591217 |        0.323379 | 0.73395  | 0.638226 | 0.523431 |
 | EleutherAI/pythia-6.9b            |        0.368 |   0.604798 |     0.608524 |    0.631548 |        0.343857 | 0.761153 | 0.6263   | 0.543567 |
-| databricks/dolly-v2-2-8b          |        0.384 |   0.611532 |     0.589582 |    0.650767 |        0.370307 | 0.742655 | 0.575535 | 0.544886 |
+| databricks/dolly-v2-3b          |        0.384 |   0.611532 |     0.589582 |    0.650767 |        0.370307 | 0.742655 | 0.575535 | 0.544886 |
 | EleutherAI/pythia-12b             |        0.364 |   0.627104 |     0.636148 |    0.668094 |        0.346416 | 0.760065 | 0.673394 | 0.559676 |
 | EleutherAI/gpt-j-6B               |        0.382 |   0.621633 |     0.651144 |    0.662617 |        0.363481 | 0.761153 | 0.655963 | 0.565936 |
 | databricks/dolly-v2-12b           |        0.408 |   0.63931  |     0.616417 |    0.707927 |        0.388225 | 0.757889 | 0.568196 | 0.56781  |
-| databricks/dolly-v2-6-9b          |        0.392 |   0.633838 |     0.607735 |    0.686517 |        0.406997 | 0.750816 | 0.644037 | 0.573487 |
+| databricks/dolly-v2-7b          |        0.392 |   0.633838 |     0.607735 |    0.686517 |        0.406997 | 0.750816 | 0.644037 | 0.573487 |
 | databricks/dolly-v1-6b            |        0.41  |   0.62963  |     0.643252 |    0.676758 |        0.384812 | 0.773667 | 0.687768 | 0.583431 |
 | EleutherAI/gpt-neox-20b           |        0.402 |   0.683923 |     0.656669 |    0.7142   |        0.408703 | 0.784004 | 0.695413 | 0.602236 |
 +-----------------------------------+--------------+------------+--------------+-------------+-----------------+----------+----------+----------+