Update README.md

README.md

@@ -4,93 +4,4 @@ license: apache-2.0
 pipeline_tag: document-question-answering
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-Better maintained files can be found here:
-https://huggingface.co/CRD716/ggml-vicuna-1.1-quantized/
-
----
-
-### NOTE:
-The PR [#1405](https://github.com/ggerganov/llama.cpp/pull/1405) brought breaking changes - none of the old models work with the latest build of llama.cpp.
-
-Pre-PR #1405 files have been marked as old but remain accessible for those who need them (oobabooga, gpt4all-chat haven't been updated to support the new format as of May 14).
-
-Additionally, `q4_3` and `q4_2` have been completely axed in favor of their 5-bit counterparts (q5_1 and q5_0, respectively).
-
-New files inference up to 10% faster without any quality reduction.
-
-
-### Links
-- [7B version of this model](https://huggingface.co/eachadea/ggml-vicuna-7b-1.1)
-- [Set up with gpt4all-chat (one-click setup, available in in-app download menu)](https://gpt4all.io/index.html)
-- [Set up with llama.cpp](https://github.com/ggerganov/llama.cpp)
-- [Set up with oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md)
-
-### Info
-- Main files are based on v1.1 release
-  - See changelog below
-  - Use prompt template: ```HUMAN: <prompt> ASSISTANT: <response>```
-- Uncensored files are based on a modified v0 release
-  - Use prompt template: ```### User: <prompt> ### Assistant: <response>```
-
-### Quantization
-Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
-
-Model | F16 | Q4_0 | Q4_1 | Q4_2 | Q4_3 | Q5_0 | Q5_1 | Q8_0
--- | -- | -- | -- | -- | -- | -- | -- | --
-7B (ppl) | 5.9565 | 6.2103 | 6.1286 | 6.1698 | 6.0617 | 6.0139 | 5.9934 | 5.9571
-7B (size) | 13.0G | 4.0G | 4.8G | 4.0G | 4.8G | 4.4G | 4.8G | 7.1G
-7B (ms/tok @ 4th) | 128 | 56 | 61 | 84 | 91 | 91 | 95 | 75
-7B (ms/tok @ 8th) | 128 | 47 | 55 | 48 | 53 | 53 | 59 | 75
-7B (bpw) | 16.0 | 5.0 | 6.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0
--- | -- | -- | -- | -- | -- | -- | -- | --
-13B (ppl) | 5.2455 | 5.3748 | 5.3471 | 5.3433 | 5.3234 | 5.2768 | 5.2582 | 5.2458
-13B (size) | 25.0G | 7.6G | 9.1G | 7.6G | 9.1G | 8.4G | 9.1G | 14G
-13B (ms/tok @ 4th) | 239 | 104 | 113 | 160 | 175 | 176 | 185 | 141
-13B (ms/tok @ 8th) | 240 | 85 | 99 | 97 | 114 | 108 | 117 | 147
-13B (bpw) | 16.0 | 5.0 | 6.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0
-
-q5_1 or 5_0 are the latest and most performant implementations. The former is slightly more accurate at the cost of a bit of performance. Most users should use one of the two.
-If you encounter any kind of compatibility issues, you might want to try the older q4_x
-
----
-
-# Vicuna Model Card
-
-## Model details
-
-**Model type:**
-Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
-It is an auto-regressive language model, based on the transformer architecture.
-
-**Model date:**
-Vicuna was trained between March 2023 and April 2023.
-
-**Organizations developing the model:**
-The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
-
-**Paper or resources for more information:**
-https://vicuna.lmsys.org/
-
-**License:**
-Apache License 2.0
-
-**Where to send questions or comments about the model:**
-https://github.com/lm-sys/FastChat/issues
-
-## Intended use
-**Primary intended uses:**
-The primary use of Vicuna is research on large language models and chatbots.
-
-**Primary intended users:**
-The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
-
-## Training dataset
-70K conversations collected from ShareGPT.com.
-(48k for the uncensored variant. 22k worth of garbage removed – see https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)
-
-## Evaluation dataset
-A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
-
-## Major updates of weights v1.1
-- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
-- Fix the supervised fine-tuning loss computation for better model quality.
+Obsolete model.