Update README.md

README.md

@@ -1,6 +1,18 @@
 ---
 license: mit
+library_name: transformers
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- llama
+- lora
+- sft
+- alignment
+- reinforcement-learning
+- peft
+- transformers
+- archer
 ---
+
 # Learning in Interaction using ArCHer
 
 > Interactive fine-tuning and alignment experiments using LoRA, supervised learning, and reinforcement-learning-based adaptation on LLaMA-3.1-8B-Instruct.
@@ -20,7 +32,7 @@ The goal of this work is to explore interactive learning paradigms for large lan
 - Preference and reward-based optimization
 - Future GRPO / ArCHer reinforcement learning methods
 
-The experiments are built on top of Meta's **LLaMA-3.1-8B-Instruct** model using the Hugging Face.
+The experiments are built on top of Meta's **LLaMA-3.1-8B-Instruct** model using the Hugging Face ecosystem.
 
 ---
 
@@ -77,7 +89,6 @@ Llama-3.1-8B-Instruct/
 
 ---
 
-
 ## Loading a Checkpoint
 
 ### Using Transformers + PEFT
@@ -107,19 +118,49 @@ tokenizer = AutoTokenizer.from_pretrained(base_model)
 
 ---
 
+## Example Inference
+
+```python
+inputs = tokenizer(
+    "Explain reinforcement learning in simple terms.",
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
 ## Notes
 
 - Only selected checkpoints and milestone runs are stored.
 - Large files are managed using Git LFS.
 - Intermediate checkpoints are omitted to reduce repository size.
+- Additional experiments and evaluation results will be added over time.
 
 ---
 
 ## Planned Work
 
-Planned extensions include:
+Future extensions include:
 
 - GRPO-based reinforcement learning
+- Human feedback integration
 - Multi-turn interaction benchmarks
 - Automated best-checkpoint selection
 - Evaluation pipelines and benchmarking suites
+
+---
+
+## License & Usage
+
+This repository follows the licensing terms of the underlying base model:
+
+- `meta-llama/Llama-3.1-8B-Instruct`
+
+Users must comply with the original Llama license and usage restrictions when using derived checkpoints.