Update README.md

README.md

@@ -1,6 +1,8 @@
 ---
 inference: false
 license: other
+datasets:
+- jondurbin/airoboros-gpt4-1.2
 ---
 
 <!-- header start -->
@@ -27,7 +29,15 @@ It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQi
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GGML)
-* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.2)
+* [Jon Durbin's unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.2)
+
+## Prompt template
+
+```
+A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
+USER: prompt
+ASSISTANT: 
+```
 
 ## How to easily download and use this model in text-generation-webui
 
@@ -145,3 +155,71 @@ Thank you to all my generous patrons and donaters!
 
 # Original model card: John Durbin's Airoboros 33B GPT4 1.2
 
+### Overview
+
+This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
+
+This is mostly an extension of [1.1](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.1) with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.
+
+The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.2), with a specific focus on:
+- coding
+- math/reasoning (using orca style ELI5 instruction/response pairs)
+- trivia
+- role playing
+- multiple choice and fill-in-the-blank
+- context-obedient question answering
+- theory of mind
+- misc/general
+
+This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with the 7b/13b versions:
+
+```
+A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT: 
+```
+
+So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
+
+### Usage
+
+To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
+```
+pip install git+https://github.com/jondurbin/FastChat
+```
+
+Be sure you are pulling the latest branch!
+
+Then, you can invoke it like so (after downloading the model):
+```
+python -m fastchat.serve.cli \
+  --model-path airoboros-33b-gpt4-1.2 \
+  --temperature 0.5 \
+  --max-new-tokens 2048 \
+  --no-history
+```
+
+Alternatively, please check out TheBloke's quantized versions:
+
+- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GPTQ
+- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GGML
+
+### Coding updates from gpt4/1.1:
+
+I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.
+
+It's not guaranteed to work all the time, but mostly it does seem to work as expected.
+
+So for example, instead of:
+```
+Implement the Snake game in python.
+```
+
+You would use:
+```
+Implement the Snake game in python.  PLAINFORMAT
+```
+
+### Other updates from gpt4/1.1:
+
+- Several hundred role-playing data.
+- A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
+- Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)