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TEST_COMMANDS.md

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+# 🧪 Quick Test Commands for Single Training Sample
+
+## Method 1: Using the Test Script (Easiest)
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+python3 test_single_sample.py
+```
+
+This will:
+- Load the first sample from `datasets/processed/split/train.jsonl`
+- Show the instruction and expected response
+- Load the fine-tuned model
+- Generate and display the output
+
+---
+
+## Method 2: Direct Inference Command
+
+### Test with a specific prompt from training data:
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+python3 scripts/inference/inference_codellama.py \
+    --mode local \
+    --model-path training-outputs/codellama-fifo-v1 \
+    --base-model-path models/base-models/CodeLlama-7B-Instruct \
+    --prompt "You are Elinnos RTL Code Generator v1.0, a specialized Verilog/SystemVerilog code generation agent. Your role: Generate clean, synthesizable RTL code for hardware design tasks. Output ONLY functional RTL code with no \$display, assertions, comments, or debug statements.
+
+Generate a synchronous FIFO with 8-bit data width, depth 4, write_enable, read_enable, full flag, empty flag, write_err flag (pulses if write when full), and read_err flag (pulses if read when empty)." \
+    --max-new-tokens 800 \
+    --temperature 0.3
+```
+
+---
+
+## Method 3: Extract Sample and Test
+
+### Extract a specific sample by line number:
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+# Extract sample 1 (first line)
+SAMPLE=$(sed -n '1p' datasets/processed/split/train.jsonl)
+INSTRUCTION=$(echo $SAMPLE | python3 -c "import sys, json; print(json.load(sys.stdin)['instruction'])")
+
+python3 scripts/inference/inference_codellama.py \
+    --mode local \
+    --model-path training-outputs/codellama-fifo-v1 \
+    --prompt "$INSTRUCTION" \
+    --max-new-tokens 800 \
+    --temperature 0.3
+```
+
+### Or use Python one-liner:
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+python3 -c "
+import json
+from pathlib import Path
+
+# Load first training sample
+with open('datasets/processed/split/train.jsonl', 'r') as f:
+    sample = json.loads(f.readline())
+    instruction = sample['instruction']
+    print('Testing with instruction:')
+    print(instruction[:200] + '...')
+    print()
+    
+# Now run inference
+import sys
+sys.path.insert(0, 'scripts/inference')
+from inference_codellama import load_local_model, generate_with_local_model
+
+model, tokenizer = load_local_model(
+    'training-outputs/codellama-fifo-v1',
+    'models/base-models/CodeLlama-7B-Instruct'
+)
+
+response = generate_with_local_model(
+    model, tokenizer, instruction,
+    max_new_tokens=800, temperature=0.3, stream=False
+)
+
+print('=' * 80)
+print('GENERATED OUTPUT:')
+print('=' * 80)
+print(response)
+"
+```
+
+---
+
+## Method 4: Interactive Mode
+
+Test interactively with your own prompts:
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+python3 scripts/inference/inference_codellama.py \
+    --mode local \
+    --model-path training-outputs/codellama-fifo-v1
+```
+
+Then type your prompt when prompted.
+
+---
+
+## Method 5: Test Specific Sample Number
+
+To test sample N from training data:
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+# Test sample 2 (change N=2 to any sample number)
+N=2
+INSTRUCTION=$(sed -n "${N}p" datasets/processed/split/train.jsonl | python3 -c "import sys, json; print(json.load(sys.stdin)['instruction'])")
+
+python3 scripts/inference/inference_codellama.py \
+    --mode local \
+    --model-path training-outputs/codellama-fifo-v1 \
+    --prompt "$INSTRUCTION" \
+    --max-new-tokens 800 \
+    --temperature 0.3
+```
+
+---
+
+## Quick Reference
+
+**Model Path:** `training-outputs/codellama-fifo-v1`  
+**Base Model:** `models/base-models/CodeLlama-7B-Instruct`  
+**Training Data:** `datasets/processed/split/train.jsonl`  
+**Test Data:** `datasets/processed/split/test.jsonl`
+
+**Recommended Parameters:**
+- `--max-new-tokens 800` (for longer code)
+- `--temperature 0.3` (deterministic code generation)
+- `--temperature 0.1` (very deterministic, try if getting text instead of code)
+