Update README.md

README.md

@@ -1,71 +1,61 @@
 ---
 base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
 library_name: peft
+license: apache-2.0
+datasets:
+- Respair/sharegpt_chatml_compressed
+- diwank/llmlingua-compressed-text
+- AlexMaclean/wikipedia-deletion-compressions
+- AlexMaclean/all-deletion-compressions
+- sentence-transformers/sentence-compression
+language:
+- en
+tags:
+- compression
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+Memories - Token Compressor for Long-Range Dependency Conversations
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
+This model is a fine-tuned version of the Llama 3.1 8B 4-bit model, specifically trained for token compression tasks. It uses LoRA (Low-Rank Adaptation) for efficient fine-tuning while maintaining the base model's performance.
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Alosh Denny
+- **Funded by [optional]:** nil
+- **Shared by [optional]:** nil
+- **Model type:** Token Compressor for Memories
+- **Language(s) (NLP):** English
+- **License:** apache-2.0
 
 ## 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]
+This model is designed for token compression tasks. It can be used to generate more concise versions of input text while preserving the essential meaning.
 
 ### 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]
+The compressed outputs from this model can be used in various NLP applications where text length is a constraint, such as summarization, efficient text storage, or as input for other language models with token limits.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model should not be used for tasks that require full preservation of the original text or where nuanced details are critical. It's not suitable for legal, medical, or other domains where precise wording is essential.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may inadvertently remove important context or nuance during compression.
+- There might be biases inherited from the base Llama 3.1 model or introduced during fine-tuning.
+- The model's performance may vary depending on the input text's domain or complexity.
 
 ### 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.
+- Users should review the compressed outputs for accuracy and appropriateness before use in critical applications.
+- It's advisable to test the model on a diverse range of inputs to understand its performance across different text types and domains.
 
 ## How to Get Started with the Model
 
@@ -77,28 +67,47 @@ Use the code below to get started with the model.
 
 ### 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]
+The model was trained on a dataset compiled from various sources, including:
+- Respair/sharegpt_chatml_compressed
+- diwank/llmlingua-compressed-text
+- AlexMaclean/wikipedia-deletion-compressions
+- AlexMaclean/all-deletion-compressions
+- sentence-transformers/sentence-compression
 
 ### 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]
+#### Preprocessing
 
+Prompt-response pairs were processed from the datasets and compiled into a single dataset (available at https://huggingface.co/datasets/aoxo/token_compressor). Unwanted characters, trailing whitespaces and inverted commas were voided.
 
 #### 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. -->
+#### Training Hyperparameters
 
-[More Information Needed]
+- **Training regime:** bf16 mixed precision
+- **Optimizer:** paged_adamw_8bit
+- **Learning rate:** 2e-4
+- **LR scheduler:** cosine
+- **Batch size:** 4 per device
+- **Gradient accumulation steps:** 16
+- **Number of epochs:** 10
+- **Max steps:** 700,472
+
+#### LoRA Configuration
+
+- **r:** 8
+- **lora_alpha:** 16
+- **lora_dropout:** 0.05
+- **bias:** none
+- **task_type:** CAUSAL_LM
+
+#### Speeds, Sizes, Times
+
+- **Total Training Compute Throughput:** 8.62 PFLOPS
+- **Total Logged Training Time:** 1422.31 hours
+- **Start Time:** 07-21-2024 02:02:32
+- **End Time:** 09-18-2024 08:21:08
+- **Checkpoint Size (adapter):** 13,648,432 bytes
 
 ## Evaluation