LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
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name
llm-integration
license
MIT
compatibility
Claude Code 2.1.220+.
description
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
Patterns for integrating LLMs into production applications: tool use, streaming, local inference, and fine-tuning. Each category has individual rule files in rules/ loaded on-demand.
# Local inference: Ollama with LangChain
llm = ChatOllama(
model="deepseek-r1:70b",
base_url="http://localhost:11434",
temperature=0.0,
num_ctx=32768,
)
# Fine-tuning: QLoRA with Unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/Meta-Llama-3.1-8B",
max_seq_length=2048, load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(model, r=16, lora_alpha=32)
Function Calling
Enable LLMs to use external tools and return structured data. Use strict mode schemas (2026 best practice) for reliability. Limit to 5-15 tools per request, validate all inputs with Pydantic/Zod, and return errors as tool results.
Run LLMs locally with Ollama for cost savings (93% vs cloud), privacy, and offline development. Pre-warm models, use provider factory for cloud/local switching.
local-ollama-setup.md -- Installation, model pulling, environment configuration
local-model-selection.md -- Model comparison by task, hardware profiles, quantization
local-gpu-optimization.md -- Apple Silicon tuning, keep-alive, CI integration
Fine-Tuning
Customize LLMs with parameter-efficient techniques. Fine-tune ONLY after exhausting prompt engineering and RAG. Requires 1000+ quality examples.
prompt-react-pattern.md -- ReAct loop for tool-using agents, thought-action-observation format
prompt-optimization.md -- Token reduction, cost optimization, model tiering, prompt spec format
Upstream coverage (do not restate)
These topics are covered by their vendors' own documentation. This skill points
at them instead of teaching them; the rules above keep only our floors, ceilings
and scars. Our delta on all of it is in references/ork-delta.md.
