Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 library_name: peft
-base_model: mistralai/Mistral-7B-Instruct-v0.1
+base_model: mistralai/Mistral-7B-Instruct-v0.2
 license: mit
 datasets:
 - irlab-udc/metahate
@@ -14,7 +14,7 @@ tags:
 # Mistral Fine-Tuned on not Engaging with Hate Speech
 
 ## Model Description
-This model is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.1` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
+This model is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.2` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
 
 ## Intended Uses & Limitations
 This model is intended for research purposes in conversational applications to stop hate speech generation.
@@ -25,17 +25,26 @@ This model is intended for research purposes in conversational applications to s
 - **False Positives/Negatives**: It's not perfect and may continue some hate speech conversations.
 - **Domain Specificity**: Performance may vary across different domains.
 
-### 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]
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, Conversation, pipeline
+
+# Load the model
+config = PeftConfig.from_pretrained("irlab-udc/Mistral-7b-Stop-Hate")
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+model = PeftModel.from_pretrained(base_model, "irlab-udc/Mistral-7b-Stop-Hate")
+tokenizer = AutoTokenizer.from_pretrained("irlab-udc/Mistral-7b-Stop-Hate")
+chatbot = pipeline(task="conversational", model=model, tokenizer=tokenizer)
+
+# Test the model
+conversation = Conversation("Your input text here")
+conversation = chatbot(conversation)
+result = conversation.messages[-1]["content"]
+```
 
 ## Training Details
 
@@ -46,39 +55,33 @@ Use the code below to get started with the model.
 [More Information Needed]
 
 ## Training Procedure
-- **Base Model:** mistralai/Mistral-7B-Instruct-v0.1
+- **Base Model:** mistralai/Mistral-7B-Instruct-v0.2
 - **Fine-Tuning:** Using PEFT approach
-- **Hardware:** Information about the hardware used
-
-#### 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 -->
-
-
-## Environmental Impact
-
-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:** RTX A6000
-- **Hours used:** 9
-- **Cloud Provider:** Private Infrastructure
-- **Carbon Efficiency (kg/kWh):** 0,432
-- **Carbon Emitted (kg eq. CO2):** 1,17
+- **Hardware:** NVIDIA RTX A6000
+- 
+#### Configurations and Hyperparameters
 
+The following LoraConfig config was used during training:
 
-## Citation
+- r: 32
+- lora_alpha: 64
+- target_modules: ["q_proj", "v_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"]
+- lora_dropout: 0.05
+- bias: "lora_only"
+- task_type: "CAUSAL_LM"
 
-If you use this model, please cite the following reference:
+The following TrainingArguments config was used during training:
 
-```bibtex
-@article{
-  SOON!
-}
-```
-
-## Training procedure
+- per_device_train_batch_size: 16
+- gradient_accumulation_steps: 1
+- warmup_steps: 5
+- max_steps: 1000
+- learning_rate: 2.5e-5
+- fp16=True
+- optim= paged_adamw_8bit
 
 The following `bitsandbytes` quantization config was used during training:
+
 - quant_method: bitsandbytes
 - _load_in_8bit: False
 - _load_in_4bit: True
@@ -96,6 +99,31 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 - PEFT 0.6.2
+- PyTorch 2.1.0
+- 🤗 Transformers 4.35.0
+- 🤗 Datasets 2.14.6
+
+
+## Environmental Impact
+
+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:** NVIDIA RTX A6000
+- **Hours used:** 9
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Efficiency (kg/kWh):** 0,432
+- **Carbon Emitted (kg eq. CO2):** 1,17
+
+
+## Citation
+
+If you use this model, please cite the following reference:
+
+```bibtex
+@article{
+  SOON!
+}
+```
 
 ## Acknowledgements
 The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).