ruggsea
/

Llama3.1-Instruct-SEP-Chat

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- philosophy
+- art
+datasets:
+- ruggsea/stanford-encyclopedia-of-philosophy_chat_multi_turn
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
 ---
+# Llama3-SEP-Chat: Philosophy Expert Assistant
+This model is a LoRA-finetuned version of `meta-llama/Meta-Llama-3.1-8B-instruct` on a curated dataset of Stanford Encyclopedia of Philosophy (SEP) conversations. The model is designed to engage in philosophical discussions with a formal yet accessible tone, leveraging the comprehensive knowledge from SEP.
+## Model Description
+The model was trained using direct finetuning on the instruct variant of Llama 3, preserving its native chat format and instruction-following capabilities while enhancing its philosophical expertise.
+### Training Dataset
+The training data consists of multi-turn conversations derived from the Stanford Encyclopedia of Philosophy, formatted as chat interactions between a user and an assistant. The conversations maintain academic rigor while ensuring accessibility.
+### Chat Format
+The model uses Llama 3's native chat format, which is automatically applied by the tokenizer. No additional tokens or formatting were added during finetuning.
 ## Training Details
+### Model Configuration
+- Base Model: `meta-llama/Meta-Llama-3.1-8B-instruct`
+- Training Type: LoRA (Low-Rank Adaptation)
+- Quantization: 4-bit (NF4)
+- Compute: Mixed Precision (bfloat16)
+### Training Hyperparameters
+- Learning Rate: 2e-5
+- Train Batch Size: 16
+- Gradient Accumulation Steps: 2
+- Effective Batch Size: 32
+- Optimizer: paged_adamw_8bit
+- Training Epochs: 5
+- Warmup Ratio: 0.03
+- LoRA Configuration:
+  - Rank: 256
+  - Alpha: 128
+  - Dropout: 0.05
+  - Target: all-linear layers
+### Framework Versions
+- Transformers: latest
+- PEFT: latest
+- PyTorch: 2.1.0+cu121
+- TRL: latest
+- Accelerate: latest
+## Usage
+The model can be used with the standard Hugging Face transformers library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "ruggsea/Llama3.1-Instruct-SEP-Chat"
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+# Format your input using the chat template
+messages = [
+    {"role": "user", "content": "What is the categorical imperative?"}
+]
+# Apply the chat template
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False
+)
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.2,
+    no_repeat_ngram_size=3,
+)
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+## Limitations
+- The model's knowledge is primarily focused on philosophical concepts and may not perform as well on general topics
+- As with all language models, it may occasionally generate incorrect or inconsistent information
+- The model inherits any limitations and biases present in the base Llama 3 model and the SEP dataset
+## License
+This model is subject to the Meta Llama 3 license terms. Please refer to Meta's licensing for usage requirements and restrictions.