Create README.md

README.md

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+---
+license: llama2
+base_model: meta-llama/Llama-2-13b-hf
+tags:
+- llama2
+- computer-engineering
+- computer-architecture
+- algorithms
+- systems
+- qora
+- lora
+- quantized
+- merged
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+- cais/mmlu
+- sahil2801/CodeAlpaca-20k
+- Open-Orca/OpenOrca
+model_type: llama
+---
+
+# Llama-2-13B-Computer-Engineering
+
+### Overview
+
+**Llama-2-13B-Computer-Engineering** is a fine‑tuned variant of **LLaMA‑2‑13B**, adapted for **computer engineering, computer architecture, systems, and algorithms**.  
+The model was trained using **QLoRA (4‑bit quantization)**, then merged into a single checkpoint.  
+This allows **13B‑scale reasoning** to run in ~6.6 GB of storage and ~16GB of GPU memory, making it usable on a single modern GPU.
+
+---
+
+### Training Setup
+- **Base model:** [LLaMA‑2‑13B](https://huggingface.co/meta-llama/Llama-2-13b-hf)  
+- **Fine‑tuning method:** QLoRA (4‑bit NF4) + LoRA adapters (`r=16`, `α=32`)  
+- **Optimized Layers:** Attention projection modules (`q_proj`, `k_proj`, `v_proj`, `o_proj`)  
+- **Final merge:** LoRA weights merged into the base model → single merged checkpoint  
+- **Resulting size:** ~6.6 GB (`safetensors` sharded files) vs. ~24 GB fp16 
+
+---
+
+## Dataset
+The dataset was curated from multiple sources to emphasize **reasoning, explanations, and code writing in computer engineering contexts**.  
+
+Included sources:
+- **MMLU (Computer Security subset)** → exam‑style questions on systems and security  
+- **CodeAlpaca‑20k (filtered)** → algorithm, data structures, complexity, trees, sorting/searching, graphs  
+- **OpenOrca subset** → reasoning tasks mentioning computer systems and architecture  
+- **Custom technical examples** (hand‑crafted) on:
+  - CPU pipelining & instruction‑level parallelism  
+  - Cache coherency and MESI protocol  
+  - Compiler optimizations (instruction scheduling, inlining, loop unrolling)  
+  - RISC vs. CISC architectures  
+  - Memory hierarchies (registers, caches, RAM, storage)  
+  - Branch prediction  
+  - Example algorithms (binary search, stacks, etc.)  
+
+
+---
+
+### Usage
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Irfanuruchi/Llama-2-13B-Computer-Engineering",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Irfanuruchi/Llama-2-13B-Computer-Engineering")
+
+prompt = """### Instruction:
+Explain CPU pipelining and its advantages.
+
+### Response:"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+---
+
+## Example Responses
+
+Q: What is cache coherency in multicore systems?
+A: Cache coherence ensures that all processor cores observe a consistent view of memory. Protocols such as MESI handle invalidation and updates when one core modifies data, preventing stale values and race conditions.
+
+Q: Implement a stack in Python.
+A: Produces a Stack class with methods for push, pop, peek, is_empty, and size.
+
+---
+
+
+## Limitations
+
+- While optimized for computer engineering, performance outside this scope is similar to the base LLaMA‑2‑13B.
+
+---
+
+## License
+
+- Base model:[Meta's LLaMA 2 license](https://huggingface.co/meta-llama/Llama-2-13b-hf).
+- Fine‑tuned weights: Distributed under the same license.
+- Datasets: Combination of open academic sets (MMLU, CodeAlpaca, OpenOrca) and custom educational material.