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Model: avinashkongara4/llama3-ragnarok-merged
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+  - llama
+  - rag
+  - question-answering
+  - natural-questions
+  - peft
+  - lora
+language:
+  - en
+license: llama3
+---
+
+# Llama3 RAGnarok — NQ Fine-tuned
+
+A fine-tuned version of **Meta Llama 3.1 8B Instruct** for **Retrieval-Augmented Generation (RAG)**,
+trained on the [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) dataset.
+
+This is the **merged model** (base + LoRA adapter baked in) — ready to use with no extra dependencies.
+
+## Model Details
+
+- **Base model:** meta-llama/Llama-3.1-8B-Instruct  
+- **Fine-tuning method:** LoRA (PEFT)  
+- **Training dataset:** Google Natural Questions (NQ)  
+- **Task:** Extractive QA / RAG  
+- **Developer:** Avinash Kongara  
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "avinashkongara4/llama3-ragnarok-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def ask(question, context):
+    prompt = f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+Context: {context}
+Question: {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
+    return tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+# Example
+context = "The Eiffel Tower is located in Paris, France. It was built in 1889."
+question = "Where is the Eiffel Tower located?"
+print(ask(question, context))
+```
+
+## Training Details
+
+- **LoRA rank:** 16  
+- **LoRA alpha:** 32  
+- **Target modules:** q_proj, v_proj, k_proj, o_proj  
+- **Training data:** Natural Questions (NQ) — answerable subset  
+- **Framework:** HuggingFace Transformers + PEFT + TRL  
+
+## Intended Use
+
+This model is designed for RAG pipelines where a context passage is retrieved
+and the model answers questions grounded in that context.
+
+## Limitations
+
+- Answers are grounded in the provided context — do not expect general knowledge answers without context
+- Best used as part of a full RAG pipeline with a retriever (e.g., FAISS, Pinecone)
+- Trained on English only
+
+## Adapter-only version
+
+The original LoRA adapter (before merging) is available at:  
+👉 [avinashkongara4/llama3-ragnarok-nq-adapter](https://huggingface.co/avinashkongara4/llama3-ragnarok-nq-adapter)