Upload model

README.md

@@ -1,54 +1,6 @@
 ---
 library_name: peft
-license: apache-2.0
-datasets:
-- ThatOneShortGuy/SongLyrics
-language:
-- en
-tags:
-- music
 ---
-# Musical Falcon
-
-[OpenAssistant/falcon-7b-sft-mix-2000](https://huggingface.co/OpenAssistant/falcon-7b-sft-mix-2000) model fine tuned using PEFT on
-[Song Lyrics](https://huggingface.co/datasets/ThatOneShortGuy/SongLyrics) to write lyrics to songs.
-
-## Model Details
-- **Finetuned from**: [OpenAssistant/falcon-7b-sft-mix-2000]
-- **Model Type**: Causal decoder-only transformer language model
-- **Language**: English (and limited capabilities in German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
-- **License**: Apache 2.0
-- **Contact**: Lol don't. This is just for fun.
-
-## Usage
-The basic basic format getting it in is:
-```python
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM
-
-config = PeftConfig.from_pretrained("ThatOneShortGuy/MusicalFalcon")
-model = AutoModelForCausalLM.from_pretrained("OpenAssistant/falcon-7b-sft-mix-2000")
-model = PeftModel.from_pretrained(model, "ThatOneShortGuy/MusicalFalcon")
-```
-
-## Prompting
-Considering this comes from [OpenAssistant/falcon-7b-sft-mix-2000](https://huggingface.co/OpenAssistant/falcon-7b-sft-mix-2000), it uses the same structure.
-Two special tokens are used to mark the beginning of user and assistant turns: `<|prompter|>` and `<|assistant|>`. Each turn ends with a `<|endoftext|>` token.
-The training prompt used the structure: 
-```
-<|prompter|>Come up with the lyrics for a song from "{artist}" {"from " + year if year else ""} titled "{title}".<|endoftext|>
-<|assistant|>Sure! Here are the lyrics:
-{lyrics}
-<|endoftext|>
-```
-
-However, it still seems to work just fine using:
-```
-<|prompter|>Write me a song titled "{title}"<|endoftext|><|assistant|>
-```
-or any anything of similar nature. Feel free to add a description of the song in there too.
-
-
 ## Training procedure
 
 
@@ -57,7 +9,7 @@ The following `bitsandbytes` quantization config was used during training:
 - load_in_4bit: False
 - llm_int8_threshold: 6.0
 - llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: True
+- llm_int8_enable_fp32_cpu_offload: False
 - llm_int8_has_fp16_weight: False
 - bnb_4bit_quant_type: fp4
 - bnb_4bit_use_double_quant: False
@@ -65,4 +17,4 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.5.0.dev0
+- PEFT 0.5.0.dev0

adapter_model.bin

@@ -1,3 +1,3 @@
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