Complete-Command-Reference.txt

# 🛠️ Complete Command Reference

## 1. Create Project Directory

```bash
mkdir -p /root/ai-tuning
cd /root/ai-tuning



cat /etc/os-release


/opt/python310/bin/python3 -m venv /root/ai-tuning/.venv310
source /root/ai-tuning/.venv310/bin/activate
pip install numpy==1.26.4
pip install pyarrow==20.0.0
pip install torch==2.6.0
pip install transformers==4.46.3
pip install datasets==2.21.0
pip install peft==0.13.2
pip install accelerate==1.0.1
pip install requests



python - <<'PY'
import numpy
import pyarrow
import torch
import transformers
import datasets
import peft
import accelerate

print("NumPy       :", numpy.__version__)
print("PyArrow     :", pyarrow.__version__)
print("PyTorch     :", torch.__version__)
print("Transformers:", transformers.__version__)
print("Datasets    :", datasets.__version__)
print("PEFT        :", peft.__version__)
print("Accelerate  :", accelerate.__version__)
print("CUDA        :", torch.cuda.is_available())
PY

. Hugging Face Login
hf auth whoami
hf auth login


Create DevOps Dataset
/root/ai-tuning/devops_dataset.jsonl
ls -lh /root/ai-tuning/devops_dataset.jsonl
wc -l /root/ai-tuning/devops_dataset.jsonl


Validate JSONL
python3 -m json.tool devops_dataset.jsonl


python3 - <<'PY'
import json

file = "/root/ai-tuning/devops_dataset.jsonl"

errors = 0

with open(file, encoding="utf-8") as f:
    for line_no, line in enumerate(f, 1):
        line = line.strip()

        if not line:
            continue

        try:
            json.loads(line)
        except Exception as e:
            print(f"Invalid JSON at line {line_no}: {e}")
            errors += 1

if errors == 0:
    print("JSONL validation successful")
else:
    print(f"Validation failed: {errors} errors")
PY






Create Train / Validation Split


python3 - <<'PY'
import json
import random

input_file = "/root/ai-tuning/devops_dataset.jsonl"

train_file = "/root/ai-tuning/train.jsonl"
validation_file = "/root/ai-tuning/validation.jsonl"

with open(input_file, encoding="utf-8") as f:
    data = [json.loads(line) for line in f if line.strip()]

random.seed(42)
random.shuffle(data)

split = int(len(data) * 0.8)

train = data[:split]
validation = data[split:]

with open(train_file, "w", encoding="utf-8") as f:
    for item in train:
        f.write(json.dumps(item, ensure_ascii=False) + "\n")

with open(validation_file, "w", encoding="utf-8") as f:
    for item in validation:
        f.write(json.dumps(item, ensure_ascii=False) + "\n")

print("Total      :", len(data))
print("Train      :", len(train))
print("Validation :", len(validation))
PY





Check Training Script
ls -lh /root/ai-tuning/train_lora.py
less /root/ai-tuning/train_lora.py


. Run LoRA Training
cd /root/ai-tuning

/opt/python310/bin/python3 train_lora.py

Check LoRA Output
ls -lah /root/ai-tuning/lora-output
ls -lh /root/ai-tuning/lora-output/adapter_model.safetensors
cat /root/ai-tuning/lora-output/adapter_config.json
du -sh /root/ai-tuning/lora-output


Test LoRA Adapter
find /root/ai-tuning/lora-output -maxdepth 1 -type f -printf "%f\n"


Merge LoRA
cd /root/ai-tuning

/opt/python310/bin/python3 merge_lora.py

Check Merged Model
du -sh /root/ai-tuning/merged-qwen-devops

ls -lh /root/ai-tuning/merged-qwen-devops
ls -lh /root/ai-tuning/merged-qwen-devops/*.safetensors



Clone llama.cpp
cd /root/ai-tuning

git clone https://github.com/ggml-org/llama.cpp.git


cd /root/ai-tuning/llama.cpp
git status



Build llama.cpp
docker run --rm -it \
  -v /root/ai-tuning:/workspace \
  ubuntu:22.04




apt-get update
apt-get install -y \
    build-essential \
    cmake \
    git \
    python3 \
    python3-pip \
    python3-dev
cd /workspace/llama.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release
ls -lh build/bin/llama-cli




Convert Merged Model to GGUF
python3 /workspace/llama.cpp/convert_hf_to_gguf.py \
    /workspace/merged-qwen-devops \
    --outfile /workspace/qwen-devops-f16.gguf \
    --outtype f16



ls -lh /root/ai-tuning/qwen-devops-f16.gguf


Quantize F16 → Q4_K_M
/workspace/llama.cpp/build/bin/llama-quantize \
    /workspace/qwen-devops-f16.gguf \
    /workspace/qwen-devops-q4_k_m.gguf \
    Q4_K_M




ls -lh /workspace/qwen-devops-*.gguf
Test GGUF Directly

/workspace/llama.cpp/build/bin/llama-cli \
    -m /workspace/qwen-devops-q4_k_m.gguf


Check Docker

docker --version
docker ps | grep ollama


docker exec $OLLAMA_CONTAINER ollama --version
docker exec $OLLAMA_CONTAINER ollama list



Create Model Directory

docker exec $OLLAMA_CONTAINER mkdir -p /models
docker exec $OLLAMA_CONTAINER ls -ld /models




Copy GGUF into Ollama Container

docker cp \
    /root/ai-tuning/qwen-devops-q4_k_m.gguf \
    $OLLAMA_CONTAINER:/models/qwen-devops-q4_k_m.gguf

docker exec $OLLAMA_CONTAINER \
    ls -lh /models/qwen-devops-q4_k_m.gguf

docker cp \
    /root/ai-tuning/Modelfile-qwen-devops \
    $OLLAMA_CONTAINER:/tmp/Modelfile

docker exec $OLLAMA_CONTAINER \
    cat /tmp/Modelfile


Create Ollama Fine-Tuned Model

docker exec -it $OLLAMA_CONTAINER \
    ollama create devops-qwen \
    -f /tmp/Modelfile

docker exec $OLLAMA_CONTAINER ollama list


Run Fine-Tuned Model


docker exec -it $OLLAMA_CONTAINER \
    ollama run devops-qwen



Ollama API


curl http://localhost:11434/api/tags

curl http://localhost:11434/api/generate \
  -d '{
    "model": "devops-qwen",
    "prompt": "How do I check disk usage in Linux?",
    "stream": false
  }'


Streaming API
curl http://localhost:11434/api/generate \
  -d '{
    "model": "devops-qwen",
    "prompt": "How do I troubleshoot Kubernetes CrashLoopBackOff?",
    "stream": true
  }'

API Health Check
curl -s http://localhost:11434/api/tags | python3 -m json.tool















Complete Pipeline Commands
# 1. Project
cd /root/ai-tuning

# 2. Activate Python
source .venv310/bin/activate

# 3. Validate dataset
python3 -c "import json; [json.loads(x) for x in open('devops_dataset.jsonl')]; print('OK')"

# 4. Train
/opt/python310/bin/python3 train_lora.py

# 5. Check adapter
ls -lh lora-output/adapter_model.safetensors

# 6. Merge
/opt/python310/bin/python3 merge_lora.py

# 7. Check merged model
du -sh merged-qwen-devops

# 8. Check GGUF
ls -lh qwen-devops-*.gguf

# 9. Check Ollama
docker exec 4333edae24c6 ollama list

# 10. Create model
docker exec -it 4333edae24c6 \
    ollama create devops-qwen -f /tmp/Modelfile

# 11. Run
docker exec -it 4333edae24c6 \
    ollama run devops-qwen

# 12. Benchmark
/opt/python310/bin/python3 benchmark_ollama.py


Final Verification Checklist

echo "===== SYSTEM ====="
uname -a
free -h
nproc

echo "===== PYTHON ====="
/opt/python310/bin/python3 --version

echo "===== DATASET ====="
wc -l /root/ai-tuning/devops_dataset.jsonl
wc -l /root/ai-tuning/train.jsonl
wc -l /root/ai-tuning/validation.jsonl

echo "===== LORA ====="
ls -lh /root/ai-tuning/lora-output/adapter_model.safetensors

echo "===== MERGED ====="
du -sh /root/ai-tuning/merged-qwen-devops

echo "===== GGUF ====="
ls -lh /root/ai-tuning/qwen-devops-*.gguf

echo "===== DOCKER ====="
docker ps

echo "===== OLLAMA ====="
docker exec 4333edae24c6 ollama list

echo "===== BENCHMARK ====="
ls -lh /root/ai-tuning/benchmark_results.json








Final Result

                    DevOps Dataset
                          │
                          ▼
                  Train / Validation
                          │
                          ▼
                Qwen2.5-3B-Instruct
                          │
                          ▼
                    LoRA Training
                          │
                          ▼
                   LoRA Adapter
                          │
                          ▼
                    Merge LoRA
                          │
                          ▼
                  Merged Qwen Model
                          │
                          ▼
                       GGUF
                          │
                          ▼
                     Q4_K_M
                          │
                          ▼
                  Ollama / Docker
                          │
                          ▼
                 devops-qwen:latest
                          │
              ┌───────────┴───────────┐
              ▼                       ▼
         Ollama API               Benchmark
              │                       │
              ▼                       ▼
          n8n / RAG          Base vs Fine-Tuned
              │
              ▼
       DevOps AI Assistant



Production Target



                    User
                     │
                     ▼
              Chat Interface
                     │
                     ▼
                n8n / FastAPI
                     │
                     ▼
                RAG Retriever
                     │
              ┌──────┴──────┐
              ▼             ▼
        Vector DB       DevOps Docs
              │
              ▼
       Relevant Context
              │
              ▼
       devops-qwen:latest
              │
              ▼
       DevOps AI Response
              │
              ▼
             User



The final architecture combines:

LoRA
 +
Qwen2.5-3B
 +
GGUF
 +
Q4_K_M
 +
Ollama
 +
Docker
 +
RAG
 +
n8n / FastAPI