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Fine-Tuning Deep Dive
The 7B Model That Actually Finishes Your Code
How I trained a model that wins 9/10 against its base
You know the feeling.
You ask a model to implement something. It starts with a paragraph explaining what the thing is. Then it opens a code block. Then... it stops mid-function.
# Move the accessed key to the end to mark it as recently used
sTRUNCATED
I got tired of this. So I fixed it.
The Fix: 50K Examples, 4 Hours
I took Qwen2.5-Coder-7B-Instruct and fine-tuned it on 50,000 high-quality code examples from the Glaive dataset.
The training:
- 50K samples, 2 epochs
- LoRA (rank 16) - only 0.5% of parameters touched
- 4 hours on an H200
The result? A model that actually finishes what it starts.
Head-to-Head: 10 Problems
I tested both models on 10 coding problems. The fine-tuned version won 9 out of 10.
| Problem | Base | v2 |
|---|---|---|
| LRU Cache | Truncated | Complete |
| Binary Search | Verbose | Clean |
| Rate Limiter | Theory only | Working code |
| Merge Sort | Truncated | Complete |
| Trie | Insert only | Insert + Search |
| Dijkstra | Truncated | Complete |
| Retry Decorator | Long docstrings | Concise |
| Connection Pool | Truncated | Complete |
| Binary Tree Serialize | Class only | Full impl |
| Thread-safe Singleton | Complete | Complete |
Real Example: Rate Limiter
Prompt: "Design a rate limiter"
Base Model:
Designing a rate limiter is crucial for maintaining the performance and stability of your system...
### 1. Define Requirements
- Rate: The maximum number of requests allowed per unit of time
- Time Window: The duration over which the rate limit applies
- Concurrency: How many clients can be processed...
Just theory. No code.
Fine-tuned (v2):
class RateLimiter:
def __init__(self, max_requests, period):
self.max_requests = max_requests
self.period = period
self.requests = []
def is_allowed(self):
current_time = int(time.time())
self.requests = [r for r in self.requests if current_time - r < self.period]
if len(self.requests) >= self.max_requests:
return False
self.requests.append(current_time)
return True
Working sliding window implementation
The Key Insight
Standard benchmarks (MMLU, HellaSwag) dropped around 2%.
But for actual code generation? 9/10 wins.
Benchmarks measure knowledge. They don't measure whether the model finishes what it starts.
Training Details
| Base Model | Qwen2.5-Coder-7B-Instruct |
| Dataset | glaive-code-assistant-v2 (50K samples) |
| Method | LoRA (r=16, alpha=32) |
| Parameters Changed | 0.5% (40M of 7.6B) |
| Epochs | 2 |
| Hardware | NVIDIA H200 |
| Training Time | Around 4 hours |
Takeaways
-
Small dataset, big impact.
50K samples changed how the model outputs code. -
Benchmarks miss output quality.
MMLU does not measure if code is complete. -
LoRA is enough.
0.5% of parameters. 4 hours. Done. -
Test on your actual use case.
Manual tests beat benchmark scores for code quality.
ResearchAudio
AI research, explained.