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shashank ms
shashank ms

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Building a Language Model with LLM: A Step-by-Step Guide

#aiinfrastructure #oxlo #ai

Building a domain-specific language model does not require training billions of parameters from scratch. The practical path starts with a strong open-source foundation model, fine-tunes it on curated data, and serves the result behind an API. This guide walks through that pipeline, from model selection to production inference, with runnable code at each stage. We will use Oxlo.ai to evaluate base models, generate synthetic training data, and host cost-effective inference.

Select a Base Model and Validate Fit

Before you write a single training script, confirm that a foundation model understands your domain. Oxlo.ai hosts 45+ open-source and proprietary models, including Llama 3.3 70B, Qwen 3 32B, DeepSeek V3.2, and DeepSeek R1 671B MoE. Because the platform is fully OpenAI SDK compatible, you can benchmark candidates with a few lines of Python.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",  # verify exact slug in Oxlo.ai docs
    messages=[
        {"role": "system", "content": "You are a medical coding assistant."},
        {"role": "user", "content": "Summarize ICD-10 category J44."}
    ]
)
print(response.choices[0].message.content)

Run this against several models to find the best starting point for your fine-tuning project.

Generate Synthetic Training Data

High-quality datasets are usually the bottleneck. A reliable way to bootstrap domain-specific data is to use a large reasoning model to generate instruction-response pairs, then curate and filter the outputs. Oxlo.ai's DeepSeek R1 671B MoE and Qwen 3 32B are well-suited for this generation step.

def generate_synthetic(prompt, model="deepseek-r1-671b"):
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7
    )
    return resp.choices[0].message.content

# Generate 100 instruction-response pairs for your domain,
# then manually review before adding them to your training corpus.

Save the results to a JSONL file with messages keys for supervised fine-tuning.

Prepare and Tokenize the Dataset

Combine real and synthetic data, then format everything into a chat template. The TRL library expects a list of message dictionaries.

from datasets import load_dataset


dataset = load_dataset("json", data_files="synthetic_data.json

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