lora-llama2-finetuned

This model is a fine-tuned instruction-following Large Language Model (LLM) specialized in generating, analyzing, and explaining Dockerfiles. It was adapted from the Llama 2 7B Chat base model using the QLoRA efficient fine-tuning method.

1. Model Description

Model ID: [Arsh014/lora-llama2-finetuned]

Base Model:

• Architecture: Llama 2 7 Billion Parameters
• Base Model Name: NousResearch/Llama-2-7b-chat-hf

Intended Use: The primary function of this model is to serve as an expert assistant for containerization tasks. It performs best when prompted with an instruction about a specific application stack or a Dockerfile snippet that needs analysis.

• Generation: Creating valid Dockerfiles from natural language descriptions (e.g., "Create a Dockerfile for a multi-stage Rust application").
• Explanation: Providing step-by-step breakdowns of existing Dockerfiles.
• Refactoring: Suggesting best practices or optimizations for Docker commands.

Limitations & Ethical Considerations: Critical Note on Scale: This model was fine-tuned on a very limited dataset (20 training examples).

• Generalization: Performance may be poor on instructions that deviate significantly from the training examples, and the model may exhibit signs of overfitting.
• Security: Generated Dockerfiles may contain insecure commands, outdated dependencies, or other security vulnerabilities. Always review and validate generated code before use in a production environment.
• Bias: The model inherits potential biases from its base model, Llama 2.

2. Training Details

The model was fine-tuned using the QLoRA (Quantized Low-Rank Adaptation) technique, which loads the base model in 4-bit precision and only trains a small set of adapter weights.

Configuration:

• Fine-Tuning Method: QLoRA (Efficiently trains adapters on a quantized base model.)
• LoRA Rank (r): 16 (Defines the rank of the update matrices.)
• LoRA Alpha (lora_alpha): 32 (Scaling factor for the LoRA weights.)
• Target Modules: ["q_proj", "v_proj"] (Only query and value attention projection layers were targeted.)
• Max Sequence Length: 512 tokens (Determines the input/output capacity.)
• Training Epochs: 3 (Number of passes over the entire dataset.)
• Final Validation Loss: 1.706886 (Indicates the loss on the small test set.)

3. Training Data

The model was trained on a custom instruction-tuning dataset designed to teach the model to follow specific prompts related to Dockerfiles.

Dataset Structure:

• Local File: /content/dockerfile_finetune.jsonl
• Format: Instruction-Response pairs, formatted for chat fine-tuning.
• Training Size: 20 examples
• Test Size: 3 examples

Prompt Template (REQUIRED for optimal results): The inference pipeline must use the following template:

Instruction:

[The user's request or question]

Input:

[The context, such as an existing Dockerfile or code snippet]

Response:

[The model's generated Dockerfile, explanation, or analysis]

4. How to Use (Inference)

Since this is a QLoRA adapter, you must load the base model (NousResearch/Llama-2-7b-chat-hf) and then merge the adapter weights from this repository.

Prerequisites: pip install torch transformers accelerate bitsandbytes peft

Inference Code (Python): (This section contains detailed Python code using transformers and peft to load and run the model. This code is essential for usage and should be copied directly.)

import torch from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel

--- Configuration ---

BASE_MODEL = "NousResearch/Llama-2-7b-chat-hf" ADAPTER_MODEL = "[YOUR_USERNAME/YOUR_REPO_NAME]" # REPLACE ME

1. Load the base model in 4-bit (QLoRA)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, device_map="auto", torch_dtype=torch.float16, load_in_4bit=True, # Critical for QLoRA )

2. Load the LoRA Adapter Weights

try: model = PeftModel.from_pretrained(model, ADAPTER_MODEL) print(f"Successfully loaded LoRA adapters from {ADAPTER_MODEL}") except Exception as e: print(f"Error loading adapter: {e}. Ensure the adapter ID is correct.") # Exit or handle error if adapter fails to load

3. Inference Function using the correct prompt template

def generate_docker_response(instruction: str, input_text: str = None) -> str: # Construct the instruction-tuning prompt template prompt = f"### Instruction:\n{instruction}\n\n" if input_text: prompt += f"### Input:\n{input_text}\n\n" prompt += "### Response:\n"

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        top_p=0.9,
        temperature=0.7,
        eos_token_id=tokenizer.eos_token_id
    )

# Decode and clean the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the content after the "### Response:" tag
response_start = response.find("### Response:\n")
if response_start != -1:
    return response[response_start + len("### Response:\n"):].strip()
return response

--- Example Usage ---

instruction = "Generate a Dockerfile for a simple Go web service that compiles a main.go file and runs it." print("--- Generating Dockerfile ---") print(generate_docker_response(instruction))

print("\n--- Explaining a Dockerfile ---") dockerfile_input = """ FROM node:20-alpine AS build WORKDIR /app COPY package*.json . RUN npm install COPY . . RUN npm run build

FROM node:20-alpine WORKDIR /app COPY --from=build /app/dist /app/dist CMD ["npm", "start"] """ instruction = "Explain this multi-stage Dockerfile step-by-step." print(generate_docker_response(instruction, dockerfile_input))