Update README.md

README.md

@@ -1,142 +1,85 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- Na0s/sft-ready-Text-Generation-Augmented-Data
+language:
+- en
+base_model:
+- mistralai/Mixtral-8x7B-Instruct-v0.1
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-<a href="https://ibb.co/vHBx7Q4"><img src="https://i.ibb.co/kxygR92/70e85f3a-7c9c-4420-9661-d30a337d8a33.jpg" alt="70e85f3a-7c9c-4420-9661-d30a337d8a33" border="0"></a>
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- 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]
+<a href="https://ibb.co/vHBx7Q4"><img src="https://i.ibb.co/kxygR92/70e85f3a-7c9c-4420-9661-d30a337d8a33.jpg" alt="70e85f3a-7c9c-4420-9661-d30a337d8a33" border="0"></a>
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Model Card for Model ID
 
-#### Preprocessing [optional]
+LoRA fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 targeting all the modules.
 
-[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]
+- **Training regime:**
+  
+```python
+quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", truncation=True, padding=True, padding_side="right")
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", quantization_config=quantization_config)
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+model = prepare_model_for_kbit_training(model)
+
+config = LoraConfig(r = 4, 
+                    lora_alpha=4, 
+                    target_modules = ["gate", "q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj"], 
+                    lora_dropout=0.1
+                    )
+
+lora_model = get_peft_model(model, config)
+
+lora_model.print_trainable_parameters()
+
+dataset = load_dataset("Na0s/sft-ready-Text-Generation-Augmented-Data", split="train")
+
+trainer = SFTTrainer(
+    model = lora_model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    packing = True,
+    args = TrainingArguments(
+        per_device_train_batch_size = 1,
+        gradient_accumulation_steps = 16,
+        group_by_length = True,
+        warmup_steps = 5,
+        bf16 = True,
+        max_steps=10000,
+        learning_rate = 2e-4,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "cosine",
+        seed = 3407,
+        eval_strategy="no",
+        do_eval=False,
+        output_dir = "./outputs",
+        push_to_hub=True,
+        remove_unused_columns=False,
+    )
+)
+
+torch.cuda.empty_cache()
+
+trainer.train()
+```
+
+
+#### Metrics and results:
+
+Upcoming.
 
 ## Environmental Impact
 
@@ -144,56 +87,10 @@ Use the code below to get started with the model.
 
 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:**
 
-[More Information Needed]
+## Technical Specifications 
 
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+### Model Architecture and Objective
 
-[More Information Needed]
+The objective of the fine-tuning of this MoE based transformer is to implement the expert pruning detailed in the following paper: [A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts](https://arxiv.org/abs/2405.16646)