Update README.md

README.md

@@ -1,19 +1,22 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-4.0
+datasets:
+- hendrycks/ethics
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
+Fine-tuned version of Phi-3-mini-4k-instruct on a subset of the hendrycks/ethics dataset
 
-
+<!--
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+<!-- 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.
 
@@ -27,7 +30,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+<!-- Provide the basic links for the model. 
 
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
@@ -35,43 +38,113 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## 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. -->
+<!-- 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. -->
+<!-- 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 -->
+<!-- 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. -->
+<!-- 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. -->
+<!-- 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. -->
+<!-- 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 (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]
+```markdown
+Install the latest version of the following python libraries:
+-torch
+-accelerate
+-peft
+-bitsandbytes
+```
+
+Run the model
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+peft_model_id = "fc91/phi3-mini-instruct-full_ethics-lora_v2.5"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+Run the model with a quantization configuration
+
+```python
+import torch, accelerate, peft 
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+from peft import PeftModel
+
+# Set up quantization configuration
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=getattr(torch, "float16")
+)
+
+# Load the base model with quantization
+base_model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-4k-instruct",
+    quantization_config=quantization_config,
+    device_map="auto",
+    attn_implementation='eager',
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+
+peft_model_id = "fc91/phi3-mini-instruct-full_ethics-lora_v2.5"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+messages = [ 
+     {"role": "system", "content": "You are a helpful AI assistant sensitive to ethical concerns. Carefully read the used prompt. Does it represent an 'ethical' or a 'unethical' reply? Respond ONLY with 'ethical' or 'unethical"}, 
+     {"role": "user", "content": [provide user content]},
+     {"role": "assistant", "content": "The user reply is..."}, 
+] 
+
+pipe = pipeline( 
+    "text-generation", 
+    model=model, 
+    tokenizer=tokenizer, 
+) 
+
+generation_args = { 
+    "max_new_tokens": 1000, 
+    "return_full_text": False, 
+    "temperature": 0.5, 
+    "do_sample": False, 
+} 
+
+# Run inference
+output = pipe(messages, **generation_args) 
+print(output[0]['generated_text']) 
+```
 
 ## Training Details
 
@@ -79,50 +152,83 @@ Use the code below to get started with the model.
 
 <!-- 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]
+["hendrycks/ethics"](https://huggingface.co/datasets/hendrycks/ethics)
+
+```markdown
+The following subsets of the above dataset were leveraged:
+-commonsense/train (13.9k random samples)
+-commonsense/validation (3.6k random samples)
+-deontology/train (18.2k random samples)
+-deontology/validation (2.8k random samples)
+-justice/train (21k random samples)
+-utilitarianism/train (21k random samples)
+```
 
 ### 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]
+<!--#### Preprocessing [optional]
 
-[More Information Needed]
+[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]
+```python
+per_device_train_batch_size=64  
+per_device_eval_batch_size=64  
+gradient_accumulation_steps=2
+gradient_checkpointing=True
+warmup_steps=100
+num_train_epochs=1
+learning_rate=0.00005
+weight_decay=0.01
+optim="adamw_hf"
+fp16=True
+```
+
+#### Speeds, Sizes, Times 
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+The overall training took 5 hours and 24 minutes.
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
+Training Loss = 0.210800
+
+Validation Loss = 0.234834	
+
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+["hendrycks/ethics"](https://huggingface.co/datasets/hendrycks/ethics)
+
+```markdown
+The following subsets of the above dataset were leveraged:
+-commonsense/test (2.5k random samples)
+-deontology/test (2.5k random samples)
+-justice/test (2.5k random samples)
+-utilitarianism/test (2.5k random samples)
+```
 
-#### Factors
+<!-- #### Factors -->
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+<!--[More Information Needed]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+<!--[More Information Needed]
 
 ### Results
 
@@ -136,13 +242,13 @@ Use the code below to get started with the model.
 
 <!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
+<!--[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).
+<!--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]
@@ -158,13 +264,13 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
-[More Information Needed]
+[More Information Needed] -->
 
 #### Hardware
 
-[More Information Needed]
+6xNVIDIA A100-SXM4-40GB
 
-#### Software
+<!--#### Software
 
 [More Information Needed]
 
@@ -172,7 +278,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+<!--**BibTeX:**
 
 [More Information Needed]
 
@@ -184,7 +290,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-[More Information Needed]
+<!--[More Information Needed]
 
 ## More Information [optional]
 
@@ -196,4 +302,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed] -->