How to Fine-Tune Llama 5 on Your Codebase

Fine-tuning Llama 5 on your own codebase can dramatically improve completion quality for internal frameworks, DSLs, and proprietary patterns. Here’s the April 2026 playbook.

Last verified: April 11, 2026

Do You Actually Need to Fine-Tune?

Before you spend money on GPUs, check if in-context learning is enough:

1. Llama 5’s 5M context window fits most entire codebases in the prompt
2. RAG with code search (cursor-style retrieval) often beats fine-tuning
3. Fine-tuning wins when you have private DSLs, house style rules, or patterns the base model doesn’t know — and when you need the knowledge compressed into weights for cost/latency reasons

Rule of thumb: Try RAG or long-context first. Fine-tune only if quality is still unacceptable after those.

Choose the Right Variant

Variant	Fine-tuning difficulty	Cost	Best for
Llama 5 8B	Easy	~$50	Fast prototypes, edge deployment
Llama 5 70B	Medium	~$150-400	Production coding assistants
Llama 5 200B MoE	Hard	~$800-1,500	High-quality specialized agents
Llama 5 600B MoE	Expert	$2,000-4,000+	Only if 70B/200B isn’t enough

For most teams, Llama 5 70B with QLoRA is the sweet spot.

Step 1: Prepare Your Data

Good fine-tuning data looks like instruction-output pairs, not raw code dumps.

Bad: {"text": "<entire repo concatenated>"}

Good:

{"instruction": "Write a handler for POST /users that validates email and saves to Postgres using our internal db client.", "input": "", "output": "import { db } from '@company/db';\nimport { validateEmail } from '@company/validators';\n\nexport async function POST(req) {\n  const { email, name } = await req.json();\n  if (!validateEmail(email)) return Response.json({error: 'bad email'}, {status: 400});\n  const user = await db.users.insert({email, name});\n  return Response.json(user);\n}"}

Target size: 5,000-50,000 examples. More isn’t always better — quality beats quantity.

How to generate pairs:

1. Extract real commits as before/after pairs
2. Use Llama 5 itself to generate instructions from existing code (self-instruct)
3. Convert your internal docs + code examples into Q&A format

Step 2: Pick a Fine-Tuning Framework

Framework	Best for	Llama 5 support
Unsloth	Solo devs, fastest single-GPU	✅ (April 10, 2026)
Axolotl	Teams, YAML configs	✅
LlamaFactory	GUI-oriented workflows	✅
TRL (HuggingFace)	Research, custom pipelines	✅

Recommendation for most teams: Unsloth for small models, Axolotl for 70B+.

Step 3: QLoRA Configuration (Llama 5 70B)

A starter Axolotl config for Llama 5 70B QLoRA:

base_model: meta-llama/Llama-5-70B-Instruct
load_in_4bit: true
adapter: qlora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj
sequence_len: 8192
micro_batch_size: 2
gradient_accumulation_steps: 8
num_epochs: 3
learning_rate: 1e-4
warmup_ratio: 0.03
optimizer: adamw_bnb_8bit

Key notes:

• 4-bit quantization keeps memory under 48GB per H100
• LoRA r=32 is the sweet spot for codebase fine-tuning
• 3 epochs is usually enough — more risks overfitting

Step 4: Run Training

On rented cloud GPUs (recommended):

• RunPod or Lambda Labs: 4x H100 at ~$10/hr
• Expected training time: 8-16 hours for 50K examples on 70B
• Total cost: ~$150-400

On your own hardware:

• 4x H100 takes ~8 hours
• 2x A100 80GB takes ~24 hours (with smaller batch)
• M3 Ultra 512GB: possible but 4-5x slower than H100

accelerate launch -m axolotl.cli.train config.yaml

Step 5: Evaluate

Don’t skip this. Use a held-out test set and compare:

• Base Llama 5 70B (zero-shot) vs your fine-tuned model
• Metrics: exact match on code completion, pass@1 on internal test suites, human eval

Red flag: If your fine-tune is worse on general tasks, you’ve overfit. Reduce epochs or LoRA rank.

Step 6: Deploy

Option A: Merge LoRA → serve with vLLM

python -m axolotl.cli.merge_lora config.yaml
vllm serve ./merged-model --max-model-len 32768

Option B: Serve LoRA adapters separately vLLM supports LoRA adapters at inference time. You can serve a base Llama 5 and hot-swap fine-tuned adapters per-team or per-project.

Common Mistakes

1. Too much data — 500K examples usually overfits; 10-50K is the sweet spot
2. Training on raw code, not instructions — always use instruction format
3. Ignoring eval — you must measure against the base model
4. Fine-tuning when RAG would do — try RAG first
5. Fine-tuning the flagship when 70B would do — 90% of use cases are fine on 70B

The Takeaway

Fine-tuning Llama 5 70B on a curated 10K-50K example dataset with QLoRA on 4x H100s costs under $400 and takes under a day. It’s the cheapest way to build a specialized coding assistant for your internal codebase in April 2026.

But try long-context prompting and RAG first. You might not need to fine-tune at all.

Last verified: April 11, 2026