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-library_name: transformers
-tags: []
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
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-## Model Details
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-### Model Description
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-<!-- 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]
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+# Llama3.1-SEP-Chat
+
+This model is a LoRA finetune of meta-llama/Meta-Llama-3.1-8B trained on multi-turn philosophical conversations. It is designed to engage in philosophical discussions in a conversational yet rigorous manner, maintaining academic standards while being accessible.
+
+## Model description
+
+The model was trained using the TRL (Transformer Reinforcement Learning) library's chat template, enabling it to handle multi-turn conversations in a natural way. It builds upon the capabilities of its predecessor [Llama3-stanford-encyclopedia-philosophy-QA](https://huggingface.co/ruggsea/Llama3-stanford-encyclopedia-philosophy-QA) but extends it to handle more interactive, back-and-forth philosophical discussions.
+
+### Chat Format
+The model uses the standard chat format with roles:
+```python
+<|system|>
+{{system_prompt}}
+<|user|>
+{{user_message}}
+<|assistant|>
+{{assistant_response}}
+```
+
+### Training Details
+The model was trained with the following system prompt:
+```
+You are an expert and informative yet accessible Philosophy university professor. Students will engage with you in philosophical discussions. Respond to their questions and comments in a correct and rigorous but accessible way, maintaining academic standards while fostering understanding.
+```
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- Learning rate: 2e-5
+- Train batch size: 1
+- Gradient accumulation steps: 4
+- Effective batch size: 4
+- Optimizer: paged_adamw_8bit
+- LR scheduler: cosine with warmup
+- Warmup ratio: 0.03
+- Training epochs: 5
+- LoRA config:
+  - r: 256
+  - alpha: 128
+  - Target modules: all-linear
+  - Dropout: 0.05
+
+### Framework versions
+- PEFT 0.10.0
+- Transformers 4.40.1
+- PyTorch 2.2.2+cu121
+- TRL latest
+- Datasets 2.19.0
+- Tokenizers 0.19.1
+
+## Intended Use
+This model is designed for:
+- Multi-turn philosophical discussions
+- Academic philosophical inquiry
+- Teaching and learning philosophy
+- Exploring philosophical concepts through dialogue
+
+## Limitations
+- The model should not be used as a substitute for professional philosophical advice or formal philosophical education
+- While the model aims to be accurate, its responses should be verified against authoritative sources
+- The model may occasionally generate plausible-sounding but incorrect philosophical arguments
+- As with all language models, it may exhibit biases present in its training data
+
+## License
+This model is subject to the Meta Llama 2 license agreement. Please refer to Meta's licensing terms for usage requirements and restrictions.
+
+## How to use
+Here's an example of how to use the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("ruggsea/Llama3.1-SEP-Chat")
+tokenizer = AutoTokenizer.from_pretrained("ruggsea/Llama3.1-SEP-Chat")
+
+
+# Example conversation
+messages = [
+    {"role": "user", "content": "What is the difference between ethics and morality?"}
+]
+
+# Format prompt using chat template
+prompt = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=False
+)
+
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```