Arsh014
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lora-llama2-finetuned

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+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:
+[YOUR_USERNAME/YOUR_REPO_NAME]
+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+* Security: Generated Dockerfiles may contain insecure commands, outdated dependencies, or other security vulnerabilities.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))