Adapting compact open-source language models for domain-specific programming tasks presents unique challenges in balancing computational efficiency with specialized capabilities. This implementation demonstrates a streamlined approach to fine-tuning Qwen2.5-Coder-7B-Instruct specifically for Go programming, emphasizing practical deployment on consumer-grade hardware.

Architecture Overview

The pipeline consists of four primary stages: data curation with compiler-verified filtering, supervised fine-tuning using QLoRA, preference optimization through compiler feedback, and optional retrieval augmentation. Each stage targets specific performance characteristics while maintaining resource constraints suitable for single-GPU environments.

Technical Specifications

• Base Model: Qwen2.5-Coder-7B-Instruct (~7B parameters)
• Fine-tuning Method: QLoRA with rank-32 adaptation on attention and feedforward projections
• Quantization: 4-bit NF4 during training, AWQ or GGUF for deployment
• Memory Requirements: 16GB VRAM for training, 8GB for inference
• Context Window: 4096 tokens training, 8192 tokens inference

Data Pipeline Architecture

Source Selection and License Compliance

The data pipeline implements strict license filtering, accepting only MIT, BSD variants, and Apache-2.0 licensed code. The-Stack-v2 provides the primary corpus, supplemented by the Go standard library (BSD-3-Clause). Repository-level deduplication uses MinHash with high similarity thresholds to eliminate near-duplicates.

Compiler-Driven Quality Gates

Each code sample undergoes multi-stage validation:

1. Syntactic validation via gofmt -l to ensure parseable Go
2. Static analysis through go vet for common errors
3. Type checking via compilation without linking
4. Linting with staticcheck for idiom compliance

Failed samples generate negative examples for preference learning rather than being discarded entirely.

Content Fingerprinting Implementation

class GoCodeProcessor:
    def __init__(self):
        self.hasher = MinHash()  # Configured for code similarity
        self.lsh = MinHashLSH()   # Threshold tuned for Go code patterns
        
    def process_repository(self, repo_path: Path) -> Iterator[Dict]:
        # Extract module boundaries from go.mod files
        modules = self._extract_module_structure(repo_path)
        
        for module in modules:
            # Process at function granularity for better context control
            functions = self._extract_functions(module)
            for func in functions:
                if self._passes_quality_gates(func):
                    signature = self._compute_signature(func)
                    if not self._is_duplicate(signature):
                        yield self._format_training_sample(func)
    
    def _extract_functions(self, module_path: Path) -> List[GoFunction]:
        # AST-based extraction preserving type information
        ast_output = subprocess.check_output(
            ["go", "doc", "-all", "-short", module_path],
            text=True
        )
        return self._parse_ast_output(ast_output)

Training Architecture

QLoRA Configuration Details

The adaptation strategy targets specific transformer components known to encode task-specific patterns:

lora_config = LoraConfig(
    r=32,                    # Rank balances expressivity vs parameters
    lora_alpha=16,          # Scaling factor for adaptation
    lora_dropout=0.05,      # Minimal dropout for stability
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",  # Attention
        "up_proj", "down_proj"                    # FFN
    ],
    modules_to_save=["lm_head", "embed_tokens"]  # Preserve vocabulary
)

Total trainable parameters: ~42M (less than 1% of base model). The configuration maintains full-precision LoRA weights while quantizing base model weights to NF4, achieving efficient memory usage during forward passes.

Fill-in-the-Middle Task Formulation

Unlike models with dedicated infill tokens, Qwen2.5-Coder requires structured prompting. The implementation uses prefix-suffix boundaries with compilation constraints:

{
    "instruction": "Complete the function body ensuring type safety and test passage.\n<PREFIX>func ParseDuration(s string) (time.Duration, error) {\n<SUFFIX>\n}\n\nfunc TestParseDuration(t *testing.T) {\n    d, _ := ParseDuration(\"1h30m\")\n    assert.Equal(t, 90*time.Minute, d)\n}",
    "output": "    parts := strings.Split(s, \"h\")\n    if len(parts) != 2 {\n        return 0, fmt.Errorf(\"invalid format\")\n    }\n    hours, _ := strconv.Atoi(parts[0])\n    minutes, _ := strconv.Atoi(strings.TrimSuffix(parts[1], \"m\"))\n    return time.Duration(hours)*time.Hour + time.Duration(minutes)*time.Minute, nil"
}

Training Dynamics

The supervised fine-tuning phase employs cosine learning rate scheduling with warmup. Key hyperparameters:

• Effective batch size: 128 (through gradient accumulation)
• Learning rate: Cosine decay from peak to near-zero
• Gradient clipping: Standard norm clipping
• Mixed precision: BF16 compute, FP32 accumulation

Convergence typically occurs within initial training epochs, monitored via perplexity on held-out Go standard library functions.

Direct Preference Optimization

Compiler-Driven Reward Signals

The preference learning phase constructs comparison pairs through automated execution:

class CompilerRewardModel:
    def score(self, code: str) -> float:
        score = 1.0
        
        # Syntactic correctness (binary gate)
        if not self._check_gofmt(code):
            return 0.0
            
        # Compilation success (primary weight)
        if self._compiles(code):
            score *= self.compilation_weight
        
        # Test passage (secondary weight)
        test_results = self._run_tests(code)
        score *= (1 + self.test_weight * test_results.pass_rate)
        
        # Static analysis (tertiary weight)
        lint_issues = self._run_staticcheck(code)
        score *= max(0.5, 1 - self.lint_penalty * len(lint_issues))
        
        # Idiomatic patterns (minor weight)
        score *= self._idiom_score(code)
        
        return score

DPO Loss Function Modification

Standard DPO uses Bradley-Terry modeling. The implementation adds margin-based ranking:

def compute_dpo_loss(policy_chosen_logps, policy_rejected_logps, 
                     reference_chosen_logps, reference_rejected_logps,
                     beta, margin):
    pi_logratios = policy_chosen_logps - policy_rejected_logps
    ref_logratios = reference_chosen_logps - reference_rejected_logps
    
    # Standard DPO component
    standard_loss = -F.logsigmoid(beta * (pi_logratios - ref_logratios))
    
    # Margin component for stronger preference signal
    margin_loss = F.relu(margin - (pi_logratios - ref_logratios))
    
    return (standard_loss + margin_weight * margin_loss).mean()

Retrieval-Augmented Generation

Document Processing Pipeline

The RAG system employs hierarchical chunking optimized for Go's structure:

1. Package-level context: Package documentation and imports (larger chunks)
2. Type definitions: Struct/interface declarations with methods (medium chunks)
3. Function implementations: Individual function bodies (smaller chunks)

Embedding generation uses BGE-small-en-v1.5 (compact model) with average pooling over subword tokens. The FAISS index implements IVF partitioning for sub-linear retrieval on large codebases.

Context Injection Strategy

Retrieved chunks integrate through structured prompting:

<context>
// Package crypto implements cryptographic primitives
package crypto

// Hash represents a cryptographic hash function
type Hash interface {
    Sum(b []byte) []byte
    Reset()
    Size() int
}
</context>

<task>
Implement SHA256 hash function adhering to the crypto.Hash interface
</task>

Performance Characteristics

Benchmark Observations

Evaluation on Go-adapted coding benchmarks demonstrates substantial improvements over the base model. The fine-tuned variant shows stronger adherence to Go idioms, particularly in error handling patterns and interface implementations. Compilation success rates increase significantly after supervised fine-tuning, with further improvements after DPO refinement.

Static analysis compliance improves markedly, especially for common issues like unused variables, inefficient slice operations, and missing error checks. The model demonstrates better understanding of Go's type system and generates more idiomatic concurrent code patterns.

Inference Optimization

Production deployment with AWQ quantization provides faster inference than GGUF while maintaining output quality. Memory footprint remains manageable on consumer GPUs. Batch processing scales efficiently with PagedAttention in vLLM, while GGUF deployment offers superior portability across hardware configurations.

Deployment Configurations

vLLM with PagedAttention

The vLLM deployment leverages continuous batching and prefix caching:

engine_args = EngineArgs(
    model="outputs/qwen2_5_coder_7b_go_final",
    tokenizer="outputs/qwen2_5_coder_7b_go_final",
    tensor_parallel_size=1,
    max_model_len=8192,
    gpu_memory_utilization=0.90,
    enforce_eager=False,  # Enable CUDA graphs
    enable_prefix_caching=True,
    max_num_batched_tokens=None,  # Auto-configure based on available memory
    swap_space=16  # GB of CPU swap for overflow
)

GGUF Quantization Strategy

Optimal quantization maintains accuracy while minimizing size:

# Convert with importance matrix from Go corpus
python convert_hf_to_gguf.py \
    --model outputs/qwen2_5_coder_7b_go_final \
    --outtype q5_K_M \
    --importance-matrix go_corpus_sample.txt \
    --outfile qwen_go_q5.gguf

# Inference configuration
./llama-server \
    -m qwen_go_q5.gguf \
    -c 8192 \
    --batch-size 512 \
    --n-gpu-layers -1 \  # Offload all layers to GPU
    --rope-scaling-type linear \
    --rope-scale 2.0

Production Integration

API Endpoint Configuration

The implementation exposes OpenAI-compatible endpoints with Go-specific parameters:

type CompletionRequest struct {
    Model       string   `json:"model"`
    Prompt      string   `json:"prompt"`
    MaxTokens   int      `json:"max_tokens"`
    Temperature float32  `json:"temperature"`
    
    // Go-specific extensions
    CompileCheck bool     `json:"compile_check"`
    TestPattern  string   `json:"test_pattern"`
    ImportPaths  []string `json:"import_paths"`
}

Editor Plugin Architecture

The LSP integration implements context-aware completion:

func (s *GoAssistantServer) Complete(ctx context.Context, 
                                     params *CompletionParams) (*CompletionList, error) {
    // Extract surrounding context
    scope := s.extractScope(params.Position)
    
    // Retrieve relevant documentation
    docs := s.ragIndex.Search(scope.CurrentFunction, topK)
    
    // Construct infill prompt
    prompt := s.buildInfillPrompt(scope, docs)
    
    // Generate with compilation validation
    completion := s.model.Generate(prompt, CompileCheck: true)
    
    return &CompletionList{Items: s.formatCompletions(completion)}, nil
}

Limitations and Future Directions

Current limitations include:

• Generics support remains inconsistent for complex type constraints
• Channel-based concurrency patterns show lower accuracy compared to sequential code
• Module versioning comprehension degrades for dependencies beyond stdlib