Build Your Own TypeScript AI Code Assistant - A Production Tutorial

Train specialized language models that match GPT-4 on TypeScript tasks and deploy them locally for complete control

Nov 29, 2025

What You’ll Build

By the end of this tutorial, you’ll have trained three production-ready TypeScript code generation models:

1.5B Model — Fast iteration assistant (30 minutes on A100)
7B Standard Model — Production code generator (3 hours on A100)
7B Reasoning Model — Advanced debugging assistant (3 hours on A100)

All three deployed to HuggingFace, running locally on your hardware, with performance comparable to GPT-4 for TypeScript-specific tasks.

What You’ll Learn

This isn’t a theoretical deep-dive, it’s a hands-on tutorial teaching you:

How to collect and filter high-quality training data from GitHub and StackOverflow
Implementing LoRA fine-tuning to train 7B models on consumer GPUs
Multi-platform optimization (Google Colab, Mac M-series, Linux)
Production deployment and performance monitoring
When to use specialized models vs general LLMs

The complete pipeline is automated and reproducible. You’ll understand every component by building it yourself.

Understanding Small Language Models (SLMs)

Before diving into building, let’s clarify what we’re working with.

What defines a Small Language Model?

The AI community defines SLMs as models ranging from 1 million to 10 billion parameters. Our TypeScript models (1.5B and 7B parameters) sit comfortably in this range. They’re “small” compared to GPT-4 (estimated 1.7 trillion parameters), but they’re far from trivial.

The SLM Advantage: Three Core Approaches

Modern SLMs achieve efficiency through three primary techniques:

Knowledge Distillation — Training smaller “student” models to mimic larger “teacher” models’ outputs
Pruning — Removing redundant or less important parameters from existing models
Fine-Tuning — Specializing pre-trained models for specific domains (what we’ll do)

We’re using the third approach: taking proven base models (Qwen 2.5 Coder, DeepSeek-R1) and specializing them exclusively for TypeScript. This gives us domain expertise without the computational cost of training from scratch.

Why SLMs Matter

The shift toward SLMs represents democratization of AI. Instead of relying on expensive API calls to massive cloud models, you can:

Run models on consumer hardware (even smartphones)
Deploy offline without internet connectivity
Maintain complete data privacy for proprietary code
Iterate rapidly with minimal infrastructure
Customize deeply for your specific use case

This tutorial shows you exactly how to leverage these advantages for TypeScript development.

Prerequisites

What You Need:

Google account (for Colab — free tier works)
GitHub account (for code access and data collection)
HuggingFace account (for model deployment)
30 minutes to 3 hours depending on model size

What You Don’t Need:

Prior machine learning experience
Expensive hardware (we’ll use Colab GPUs)
Large datasets (we’ll collect them automatically)
Deep understanding of transformers (tutorial explains everything)

Part 1: Understanding the Performance Gap

Before we start building, let’s understand why this matters.

The Production Reality

I deployed these models in production environments for three months. Here’s what the data shows:

Why the performance gap with older models?

General models trained on everything perform adequately at everything but excel at nothing. Let me show you a concrete example:

// Prompt: “Create a React hook for fetching user data”

// GPT-3.5 Output (older model):
function useUserData(userId) {  // Missing types
  const [user, setUser] = useState();  // No type parameter

  useEffect(() => {
    fetchUser(userId).then(setUser);
  }, []); // Missing dependency: userId

  return user;
}

// Our Specialized SLM Output:
interface User {
  id: string;
  name: string;
  email: string;
}

function useUserData(userId: string) {
  const [user, setUser] = useState<User | null>(null);
  const [loading, setLoading] = useState(true);
  const [error, setError] = useState<Error | null>(null);

  useEffect(() => {
    let cancelled = false;

    const loadUser = async () => {
      try {
        setLoading(true);
        const data = await fetchUser(userId);
        if (!cancelled) {
          setUser(data);
        }
      } catch (err) {
        if (!cancelled) {
          setError(err instanceof Error ? err : new Error(’Failed to fetch’));
        }
      } finally {
        if (!cancelled) {
          setLoading(false);
        }
      }
    };

    loadUser();

    return () => {
      cancelled = true; // Cleanup prevents memory leaks
    };
  }, [userId]); // Correct dependencies

  return { user, loading, error };
}

Notice the differences:

Proper TypeScript interfaces
Complete type annotations
Correct useEffect dependencies
Error handling and loading states
Cleanup function to prevent memory leaks

This isn’t cherry-picked — our models generate this level of quality systematically because they’re trained exclusively on production-quality TypeScript code.

Part 2: Setting Up Your Training Environment

Let’s get your environment ready. This takes about 2 minutes.

Step 1: Clone the Repository

Open Google Colab (https://colab.research.google.com/) and create a new notebook.

# Mount Google Drive to persist your models
from google.colab import drive
drive.mount(’/content/drive’)

# Navigate and clone
%cd /content/drive/MyDrive/
!git clone https://github.com/sylvester-francis/slm-typescript-model.git slm_code
%cd slm_code

# Verify you’re in the right place
!pwd

Step 2: Configure Your API Tokens

Click the key icon in the left sidebar to open Colab Secrets.

Add these three secrets:

GITHUB_TOKEN

Go to: https://github.com/settings/tokens
Click “Generate new token (classic)”
Select scope: repo (Full control of private repositories)
Copy the token and paste it into Colab Secrets

HF_TOKEN

Go to: https://huggingface.co/settings/tokens
Click “New token”
Select: Write access
Copy and paste into Colab Secrets

STACKOVERFLOW_KEY (Optional)

Only needed if you want StackOverflow data
Register at: https://stackapps.com/apps/oauth/register
Can skip for now

Step 3: Verify Your Environment

# Check GPU availability
!nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

Expected output: Something like Tesla T4, 15360 MiB or Tesla A100-SXM4-40GB, 40960 MiB

If you see “NVIDIA-SMI has failed”:

Go to Runtime → Change runtime type
Select “GPU” under Hardware accelerator
Save and re-run the cell

Checkpoint: You should now have:

Repository cloned to Google Drive
API tokens configured in Colab Secrets
GPU detected and available
All dependencies installed

Part 3: Training Your First Model (1.5B)

Now let’s train your first model. This model is perfect for learning because it trains fast and uses minimal GPU memory.

Understanding What Happens During Training

Before we run the command, let’s understand the pipeline:

Data Collection — Pulls TypeScript code from GitHub repos with 10k+ stars
Quality Filtering — Scores each sample, keeps only top 25%
LoRA Preparation — Sets up parameter-efficient fine-tuning adapters
Training Loop — 3 epochs through 2,000 high-quality samples
Evaluation — Tests on held-out samples, calculates metrics
Upload — Deploys to HuggingFace automatically

Total time: 20-30 minutes on A100, 60-90 minutes on T4.

Run the Training

# Complete automated pipeline
!python colab/colab_train_and_upload.py

What you’ll see:

[OK] Mounting Google Drive...
[OK] Environment validation passed
[OK] Collecting TypeScript samples from GitHub...
Found 5,234 potential samples
[OK] Quality filtering (target: 2,000 samples)...
Filtered to 2,147 high-quality samples

[OK] Loading base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
[OK] Setting up LoRA configuration (r=64, alpha=16)
[OK] Training dataset: 2,000 samples

Training Configuration:
 Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 Batch size: 4
 Gradient accumulation: 8
 Effective batch size: 32
 Learning rate: 2e-4
 Max sequence length: 1024
 LoRA rank: 64

Starting training...
Epoch 1/3: 100% 63/63 [08:42<00:00, 0.12it/s, loss=0.752]
Epoch 2/3: 100% 63/63 [08:38<00:00, 0.12it/s, loss=0.421]
Epoch 3/3: 100% 63/63 [08:41<00:00, 0.12it/s, loss=0.298]

[OK] Training completed in 26 minutes
[OK] Model saved to: ./models/typescript-slm-1.5b
[OK] Uploading to HuggingFace: your-username/typescript-slm-1.5b
[OK] Upload complete!

Model available at: https://huggingface.co/your-username/typescript-slm-1.5b

Understanding the Training Loss:

Epoch 1: loss=0.752 — Model is learning patterns
Epoch 2: loss=0.421 — Significant improvement
Epoch 3: loss=0.298 — Converging to optimal weights

Good training shows steadily decreasing loss. If loss increases or plateaus early, you might have learning rate issues (rare with our defaults).

Testing Your Model

Let’s verify it works:

# Load your newly trained model
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = “Qwen/Qwen2.5-Coder-1.5B-Instruct”
model = AutoModelForCausalLM.from_pretrained(base_model, device_map=”auto”)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load your adapter
model = PeftModel.from_pretrained(model, “./models/typescript-slm-1.5b”)

# Test it
prompt = “Create a TypeScript interface for a user profile with name, email, and avatar:”
inputs = tokenizer(prompt, return_tensors=”pt”).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected output:

interface UserProfile {
  name: string;
  email: string;
  avatar?: string;
  id: string;
  createdAt: Date;
  updatedAt: Date;
}

Checkpoint: You now have:

Trained 1.5B model saved locally
Model deployed to HuggingFace
Verified model generates valid TypeScript
Understanding of the training pipeline

Part 4: Understanding the Data Quality System

Now that you’ve trained a model, let’s understand what makes the training data high-quality. This is the secret sauce that makes specialized models perform at GPT-4 levels for specific tasks.

The Quality Scoring Algorithm

Every code sample goes through multi-dimensional scoring:

# From scripts/filter_dataset.py
def calculate_quality_score(code_sample: str) -> float:
    “”“
    Scores TypeScript code across 4 dimensions
    Returns: 0.0 (worst) to 1.0 (best)
    “”“
    scores = {
        ‘type_density’: measure_type_annotation_density(code_sample),
        ‘completeness’: check_module_completeness(code_sample),
        ‘patterns’: detect_framework_patterns(code_sample),
        ‘modernity’: check_es6_features(code_sample),
    }

    # Weighted scoring
    weights = {
        ‘type_density’: 0.35,   # Most important
        ‘completeness’: 0.25,   # Complete modules, not fragments
        ‘patterns’: 0.25,       # Framework best practices
        ‘modernity’: 0.15       # ES6+ features
    }

    return sum(scores[k] * weights[k] for k in scores)

Filtering in Action

Let’s run the filter on some sample data:

# Collect raw samples
!python cli.py collect

# This gives you: data/raw/github_typescript.jsonl
# Approximately 8,000-12,000 samples

# Now filter for quality
!python cli.py preprocess

# This outputs to: data/processed/train.jsonl
# Approximately 2,000-3,000 high-quality samples (top 25%)

View the quality distribution:

import json

# Load and analyze
with open(’data/processed/train.jsonl’) as f:
    samples = [json.loads(line) for line in f]

print(f”Total high-quality samples: {len(samples)}”)
print(f”Average quality score: {sum(s[’quality_score’] for s in samples) / len(samples):.3f}”)
print(f”\nFramework distribution:”)
for framework in [’react’, ‘angular’, ‘nextjs’, ‘typescript’, ‘nodejs’]:
    count = sum(1 for s in samples if framework in s.get(’tags’, []))
    percentage = (count / len(samples)) * 100
    print(f”  {framework.title()}: {count} samples ({percentage:.1f}%)”)

Expected output:

Total high-quality samples: 2,147
Average quality score: 0.823

Framework distribution:
  React: 1,243 samples (57.9%)
  Angular: 891 samples (41.5%)
  Nextjs: 486 samples (22.6%)
  Typescript: 301 samples (14.0%)
  Nodejs: 187 samples (8.7%)

This quality filtering is why our models perform at GPT-4 levels for TypeScript — we train only on production-grade code, not everything on the internet.

Part 5: Training the Production Model (7B)

Now that you understand the pipeline, let’s train the production-quality 7B model. This requires an A100 GPU.

Check Your GPU

!nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

Required: Tesla A100-SXM4-40GB, 40960 MiB (or similar A100)

If you see T4: The T4 has only 16GB VRAM, insufficient for 7B models. You’ll need Colab Pro for A100 access, or stick with the 1.5B model (which works great on T4).

Understanding LoRA Parameters

Before training, let’s understand what we’re configuring:

# LoRA Configuration for 7B model
lora_config = {
    ‘r’: 64,              # LoRA rank - adapter matrix dimension
    ‘alpha’: 16,          # Scaling factor
    ‘dropout’: 0.1,       # Regularization
    ‘target_modules’: [   # Which layers to adapt
        ‘q_proj’,         # Query projection
        ‘k_proj’,         # Key projection
        ‘v_proj’,         # Value projection
        ‘o_proj’,         # Output projection
        ‘gate_proj’,      # MLP gate
        ‘up_proj’,        # MLP up
        ‘down_proj’       # MLP down
    ]
}

Full fine-tuning vs LoRA:

Training Command

# 7B Standard Model (best for code generation)
!python colab/colab_train_7b.py

This will:

Use the medium dataset (5,000 samples) instead of small (2,000)
Train for 2-3 hours on A100
Use higher LoRA rank (64 vs 32)
Support 2048 token context (vs 1024)

Testing the 7B Model

Let’s test it with a complex prompt:

prompt = “”“Create a Next.js API route that:
1. Accepts POST requests with user registration data
2. Validates input using zod
3. Hashes password with bcrypt
4. Saves to database
5. Returns JWT token
“”“

inputs = tokenizer(prompt, return_tensors=”pt”).to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected output:

import { NextApiRequest, NextApiResponse } from ‘next’;
import { z } from ‘zod’;
import bcrypt from ‘bcrypt’;
import jwt from ‘jsonwebtoken’;
import { prisma } from ‘@/lib/prisma’;

const registerSchema = z.object({
  email: z.string().email(’Invalid email format’),
  password: z.string().min(8, ‘Password must be at least 8 characters’),
  name: z.string().min(2, ‘Name must be at least 2 characters’),
});

interface RegisterResponse {
  success: boolean;
  token?: string;
  error?: string;
}

export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse<RegisterResponse>
) {
  if (req.method !== ‘POST’) {
    return res.status(405).json({
      success: false,
      error: ‘Method not allowed’
    });
  }

  try {
    // Validate input
    const validation = registerSchema.safeParse(req.body);
    if (!validation.success) {
      return res.status(400).json({
        success: false,
        error: validation.error.errors[0].message
      });
    }

    const { email, password, name } = validation.data;

    // Check if user exists
    const existing = await prisma.user.findUnique({ where: { email } });
    if (existing) {
      return res.status(400).json({
        success: false,
        error: ‘Email already registered’
      });
    }

    // Hash password
    const hashedPassword = await bcrypt.hash(password, 10);

    // Create user
    const user = await prisma.user.create({
      data: {
        email,
        password: hashedPassword,
        name
      }
    });

    // Generate JWT
    const token = jwt.sign(
      { userId: user.id, email: user.email },
      process.env.JWT_SECRET!,
      { expiresIn: ‘7d’ }
    );

    return res.status(201).json({
      success: true,
      token
    });

  } catch (error) {
    console.error(’Registration error:’, error);
    return res.status(500).json({
      success: false,
      error: ‘Internal server error’
    });
  }
}

Notice:

Comprehensive input validation with zod
Proper error handling and status codes
Security best practices (password hashing, JWT)
Database operations with Prisma
TypeScript types throughout

This is production-ready code generated by your specialized model.

Part 6: Training the Reasoning Model (Advanced)

The reasoning model adds chain-of-thought capabilities for debugging and code review.

Understanding Reasoning Models

Standard models predict the next token. Reasoning models generate internal thought processes:

Standard model:

// User: “Why does this component re-render infinitely?”

// Direct answer (may miss nuances)
The useEffect is missing a dependency array.

Reasoning model:

// User: “Why does this component re-render infinitely?”

/*
REASONING PROCESS:
1. Examining useEffect hook - no dependency array provided
2. Without deps, effect runs after every render
3. Effect calls setUsers(), triggering state update
4. State update causes re-render
5. Re-render triggers effect again (infinite loop)

IMPACT ANALYSIS:
- Continuous network requests
- Performance degradation
- Potential rate limiting
- Memory leaks if async operations not cleaned up

SOLUTION:
Add empty dependency array for mount-only execution
Implement cleanup function for async operations
*/

// Then provides corrected code with detailed explanation

Switch to Reasoning Base Model

Edit colab/colab_train_7b.py:

# Change line 35 from:
MODEL_VARIANT = “standard”

# To:
MODEL_VARIANT = “reasoning”

Or use sed:

!sed -i ‘s/MODEL_VARIANT = “standard”/MODEL_VARIANT = “reasoning”/’ colab/colab_train_7b.py

Train Reasoning Model

!python colab/colab_train_7b.py

Key differences:

Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Output repo: typescript-slm-7b-reasoning
Same training time as standard 7B

Part 7: Local Deployment and Production Use

Now let’s deploy your models locally for real development use.

Option 1: Python API Server

Create server.py:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

app = FastAPI()

# Load model at startup
MODEL_PATH = “./models/typescript-slm-7b”
BASE_MODEL = “Qwen/Qwen2.5-Coder-7B-Instruct”

print(”Loading model...”)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map=”auto”,
    torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(model, MODEL_PATH)
model.eval()
print(”Model loaded successfully”)

class GenerateRequest(BaseModel):
    prompt: str
    max_tokens: int = 256
    temperature: float = 0.7

class GenerateResponse(BaseModel):
    code: str
    tokens_generated: int

@app.post(”/generate”, response_model=GenerateResponse)
async def generate_code(request: GenerateRequest):
    try:
        inputs = tokenizer(request.prompt, return_tensors=”pt”).to(model.device)

        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=request.max_tokens,
                temperature=request.temperature,
                do_sample=True
            )

        generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
        tokens = len(outputs[0])

        return GenerateResponse(
            code=generated,
            tokens_generated=tokens
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get(”/health”)
async def health():
    return {”status”: “healthy”, “model”: MODEL_PATH}

if __name__ == “__main__”:
    import uvicorn
    uvicorn.run(app, host=”0.0.0.0”, port=8000)

Run the server:

pip install fastapi uvicorn pydantic
python server.py

Option 2: Edge Deployment with Ollama (Offline-First)

For completely offline deployments on PCs or edge devices, use Ollama:

Why Ollama?

Zero-config offline operation
Automatic GPU optimization
Model version management
REST API included
Cross-platform (Mac, Linux, Windows)

Convert Your Model to Ollama Format:

# Install Ollama
curl https://ollama.ai/install.sh | sh

# Create Modelfile
cat > Modelfile <<EOF
FROM ./models/typescript-slm-7b
TEMPLATE “”“{{ .Prompt }}”“”
PARAMETER temperature 0.7
PARAMETER top_p 0.9
EOF

# Create Ollama model
ollama create typescript-slm -f Modelfile

# Test it
ollama run typescript-slm “Create a TypeScript interface for a blog post”

Ollama advantages:

Models persist across sessions
Automatic model updates and versioning
Built-in caching for faster repeated queries
Works completely offline once downloaded
No Python dependencies needed for inference

Understanding SLM Limitations and When to Use General LLMs

Before we celebrate, let’s be realistic about what SLMs can and cannot do.

When SLMs Excel

Domain-specific tasks — TypeScript generation, React patterns, framework-specific code
Latency-critical applications — Real-time IDE assistance, live code completion
Privacy-sensitive contexts — Proprietary codebases, regulated industries, offline requirements
Repeatable patterns — Code following established conventions and best practices

When to Use GPT-4/GPT-5

Broad knowledge requirements — Cross-domain questions spanning multiple unrelated technologies
Complex reasoning — Architectural decisions requiring deep context across heterogeneous systems
Novel problem-solving — Completely new challenges outside training distribution
Natural language tasks — Long-form documentation, creative writing, concept explanation

Hybrid Architecture (Recommended for Production)

The optimal production setup combines both:

interface AIRequest {
  type: ‘typescript’ | ‘python’ | ‘documentation’ | ‘architecture’;
  prompt: string;
}

async function routeToOptimalModel(request: AIRequest) {
  switch (request.type) {
    case ‘typescript’:
      // Use specialized TypeScript SLM (fast, accurate, local)
      return await typescriptSLM.generate(request.prompt);

    case ‘python’:
      // Use general LLM (not our specialty)
      return await gpt4.generate(request.prompt);

    case ‘documentation’:
      // Use LLM for natural language
      return await gpt5.generate(request.prompt);

    case ‘architecture’:
      // Use LLM for complex reasoning across domains
      return await claude.generate(request.prompt);
  }
}

This approach gives you:

Best-in-class accuracy (right tool for each job)
Optimal latency (local SLM for common tasks)
Complete privacy (sensitive code stays local)
Graceful fallback (LLM handles edge cases)

Conclusion: You’ve Built Production AI

Congratulations! You’ve completed the full pipeline:

Trained three specialized models (1.5B, 7B standard, 7B reasoning)
Deployed to HuggingFace for public access
Set up local inference for private, fast code generation
Understanding when to use specialized vs general models

What You’ve Learned

Technical Skills:

LoRA fine-tuning for parameter-efficient training
Data quality scoring and filtering systems
Multi-platform GPU optimization
Production deployment strategies
Performance benchmarking and analysis

Practical Knowledge:

When to use specialized vs general models
Efficient AI training strategies
Production deployment considerations
Privacy-first local deployment

Next Steps

Expand Your Models:

Train on your company’s codebase for domain expertise
Create framework-specific specialists (React-only, Angular-only)
Experiment with different base models (Llama, Mistral)
Build multilingual models (TypeScript + Python + Rust)

Enhance Your Pipeline:

Implement automated quality validation
Set up A/B testing infrastructure
Create evaluation benchmarks specific to your use case
Build CI/CD integration for code generation

Share Your Results:

Publish your models to HuggingFace
Write about your experience and metrics
Contribute improvements back to the open-source pipeline
Help others replicate your success

Resources

Models:

Code:

Community:

Open issues for questions or improvements
Share your trained models and results
Contribute dataset improvements or new features

The specialized AI revolution isn’t coming — it’s here. And now you’re part of it.

Sylvester Francis builds production AI systems focused on practical deployment and measurable performance. Follow for more hands-on tutorials on specialized AI, developer tooling, and production ML engineering.

Appendix: Troubleshooting Common Issues

Issue: “CUDA out of memory”

# Solution: Reduce batch size
!python cli.py train \
    --batch-size 1 \
    --gradient-accumulation 32 \
    --lora-r 32  # Reduce from 64

Issue: “Training loss not decreasing”

# Check learning rate - try reducing:
!python cli.py train --learning-rate 1e-4  # Default: 2e-4

# Or increase epochs:
!python cli.py train --epochs 5  # Default: 3

Issue: “Model generating invalid syntax”

# Use lower temperature for more conservative generation:
outputs = model.generate(
    **inputs,
    temperature=0.5,  # Lower = more conservative
    top_p=0.85        # Lower = more focused
)

References and Further Reading

Jjokah, J. (2024). “Small Language Models: An Introduction.” HuggingFace Blog. Link
Hu, E. J., et al. (2021). “LoRA: Low-Rank Adaptation of Large Language Models.” arXiv:2106.09685. Link
Qwen Team. (2024). “Qwen2.5-Coder: Technical Report.” Alibaba Cloud.
DeepSeek. (2024). “DeepSeek-R1: Scaling Reinforcement Learning with Reasoning.” arXiv.

Complementary Resources:

Ollama Documentation - Local model deployment
PEFT Library - Parameter-efficient fine-tuning
TRL Documentation - Transformer reinforcement learning
SLM Landscape Overview - Broader context on SLMs

Topics: #MachineLearning #TypeScript #AI #Tutorial #CodeGeneration #LoRA #SmallLanguageModels #React #NextJS #DeveloperTools #ProductionAI

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