Update README.md

README.md

@@ -2,199 +2,160 @@
 library_name: transformers
 tags:
 - llama-factory
+- not-for-all-audiences
+license: llama3
+language:
+- en
 ---
 
-# Model Card for Model ID
+# L3 8B Celeste
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
+We trained [LLaMA 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) at 8K context using [Reddit Writing Prompts](https://huggingface.co/datasets/nothingiisreal/Reddit-Dirty-And-WritingPrompts), [Opus 15K Instruct](https://huggingface.co/datasets/nothingiisreal/Claude-3-Opus-Instruct-15K) and [c2 logs cleaned](https://huggingface.co/datasets/nothingiisreal/c2-logs-cleaned)
 
-## Model Details
+# Usage Tips
 
-### Model Description
+## System Message / Jailbreak
+<p style="font-size: 20px; color: red; font-weight: bold;">
+    IF THE GENERATIONS ARE BAD, REMOVE ALL SYSTEM PROMPTS
+</p>
+Particularly SillyTavern default prompts can make the model worse. Claude Jailbreaks should work fine though, there was a lot of them in c2 logs. 
 
-<!-- Provide a longer summary of what this model is. -->
+**You don't need a JB for casual usage but a JB can steer behaviour still.**
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Sampling
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+You **should** also try messing with different settings.
 
-### Model Sources [optional]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630cf5d14ca0a22768bbe10c/uzVgp1ZMNV_LRx1stLxJ6.png)
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+# Train Data
+The split was as follows:
 
-[More Information Needed]
+- **2K rows from r/WritingPrompts**
+- **2K rows from r/DirtyWritingPrompts**
+- **2K rows from Opus Instruct 15K (specifically the 6.5K jsonl)**
+- **2K rows from c2 logs cleaned**
 
-**APA:**
+While we did train all system prompts from c2 logs we also have our own system prompts.
+<details>
+  <summary>List of trained system prompts. Note: c2 logs system prompts and char cards were also included.</summary>
 
-[More Information Needed]
+### reddit_dirty_writing_prompts.jsonl
+**2000**  
+**"You are a short story writer. Write a story based on prompt provided by user below.  
+Mode: NSFW"**
 
-## Glossary [optional]
+### reddit_writing_prompts.jsonl
+**2000**  
+**"You are a short story writer. Write a story based on prompt provided by user below.  
+Mode: SFW"**
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Opus_Instruct-v2-6.5K-Filtered-v2.jsonl
+**2000**  
+**""** (no prompt)
 
-[More Information Needed]
+### deduped-c2-logs-maywell-final-filter-4.jsonl
+**2000**  
+(Only if there was no system prompt in the conversation, otherwise keep original system prompt)
+**"You are an expert actor that can fully immerse yourself into any role given. You do not break character for any reason, even if someone tries addressing you as an AI or language model."**
 
-## More Information [optional]
+</details>
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+# Our Findings and Experimentation results
+
+## Preface
+
+We think there is too much secrecy around what data is being used, and different training methods. So we decided to share as much as possible.
+
+## Findings
+
+### The Good
+We found that increasing the amount of ranks from 64 to 256 has reduced repetition but also led to the language used resembling Claude more than the 64 rank version. No worries, it's still far enough from Claude.
+
+It also led to increased coherency but reduced instruction following, likely because the model started diverging more away from L3 8B Instruct.
+
+**The model is uncensored for RP. For Instruct it needs 2-3 words of prefill for the first message.**
+
+We found that increasing the amount of data from 1K to 8K reduced repetition aswell.
+
+**The prose is much better than other synthetic data generations. The model also demonstrates increased style copying abilities likely a result of the long data and varying writing styles found in WritingPrompts.**
+
+**The model is exceptional at being creative in roleplaying**, knows different persona's and even a single character will change persona in different contexts, persona is tied to last few messages rather than system message or character card. **This is great as it often means the model can do impressive things without you needing to explicitly specify.**
+
+### Improvements for Next Run
+
+Formatting can break sometimes.
+
+### Comments about training
+
+Grad norm kept increasing throughout the run which is concerning, albeit it could be a side effect of the LR getting lower due to cosine LR Scheduler.
+
+## Graphs
+Colors:
+<p style="color: #F0B899;">256 rank on 8K rows</p>
+<p style="color: #5BC5DB;">64 rank on 8K rows</p>
+<p style="color: #5387DD;">64 rank on 1K rows</p>
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630cf5d14ca0a22768bbe10c/y9hC4bGq-Lt7sDQ23q5db.png)
+
+## Main training Command
+
+**Hardware Used:** 4xH100 NVL for 2 hours.
+
+Here is the command, edit rank, learning rate, and any other parameter as you wish.
+```
+!FORCE_TORCHRUN=1 llamafactory-cli train \
+    --stage sft \
+    --do_train True \
+    --model_name_or_path NousResearch/Meta-Llama-3-8B-Instruct \
+    --preprocessing_num_workers 16 \
+    --finetuning_type lora \
+    --quantization_method bitsandbytes \
+    --use_rslora False \
+    --lora_rank 64 \
+    --lora_alpha 64 \
+    --lora_dropout 0.1 \
+    --lora_target all \
+    --template llama3 \
+    --flash_attn fa2 \
+    --deepspeed examples/deepspeed/ds_z3_config.json \
+    --use_unsloth False \
+    --dataset_dir /workspace/sft \
+    --dataset dataset_name \
+    --cutoff_len 8192 \
+    --learning_rate 4e-6 \
+    --lr_scheduler_type cosine \
+    --num_train_epochs 2.0 \
+    --max_samples 100000 \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 1 \
+    --logging_steps 3 \
+    --save_steps 500 \
+    --warmup_ratio 0.05 \
+    --val_size 50 \
+    --eval_strategy steps \
+    --eval_steps 0.05 \
+    --optim adamw_bnb_8bit \
+    --packing False \
+    --train_on_prompt False \
+    --report_to all \
+    --max_grad_norm 1.0 \
+    --output_dir saves/LLaMA3-8B/trained-models/8krows-dwrp-c2l-opus-lora-4e-6-cosine-24-normal-bs \
+    --bf16 True \
+    --plot_loss True \
+    --ddp_timeout 180000000 \
+    --per_device_eval_batch_size 4 \
+    --include_num_input_tokens_seen True
+```
 
-[More Information Needed]
+---
 
-## Model Card Contact
+Wow, you've read all of that? You seem like the person that would join our [discord](https://discord.gg/YcrXhk7QD7)
 
-[More Information Needed]
+Gemma 9B and 27B at some point? ;)