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xTuring
Fine‑tune, evaluate, and run private LLMs effortlessly
xTuring provides a simple API to fine‑tune, evaluate, and deploy open‑source LLMs privately, supporting LoRA, INT8/INT4 quantization, CPU inference, and scalable GPU workloads.
Overview
Highlights
Simple Python API for data prep, training, inference, and evaluation
Private‑by‑default execution on local machines or VPCs
Efficient fine‑tuning with LoRA and INT8/INT4 weight‑only quantization
Scalable from CPU/laptop to multi‑GPU clusters, with CPU‑only inference via Intel Extension
Pros
- Easy-to-use high‑level abstractions reduce engineering effort
- Strong privacy guarantees; no data leaves your infrastructure
- Cost‑effective training through low‑precision techniques
- Flexible hardware support from CPUs to large GPU farms
Considerations
- Large models still demand substantial GPU memory and compute
- Evaluation currently limited to perplexity metric
- Advanced quantization configs may require Intel‑specific libraries
- Documentation may assume familiarity with LLM fine‑tuning concepts
Managed products teams compare with
When teams consider xTuring, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams building internal chatbots or assistants on proprietary data
- Researchers experimenting with LoRA and quantization on open‑source models
- Enterprises needing on‑prem inference for compliance reasons
- Developers prototyping LLM behavior on modest hardware
Not ideal when
- Users seeking a fully managed hosted API service
- Projects without any GPU resources for medium‑size models
- Scenarios requiring extensive evaluation metrics beyond perplexity
- Non‑technical stakeholders without programming expertise
How teams use it
Internal FAQ chatbot with LLaMA 2
Fine‑tuned private assistant that answers company‑specific questions with low latency
Edge deployment of Falcon 7B using INT4
Reduced model size and inference cost, enabling real‑time responses on limited hardware
Perplexity evaluation of GPT‑OSS 20B on custom dataset
Quantitative insight into model fit before committing to production
CPU‑only prototype with distilgpt2
Rapid iteration and testing without GPU resources
Tech snapshot
Python100%
Frequently asked questions
Do I need a GPU to use xTuring?
Small models (e.g., distilgpt2) run on CPU, but large models benefit from GPU acceleration.
Which quantization options are supported?
xTuring supports LoRA adapters, INT8, INT4, and combinations such as LoRA+INT8 or LoRA+INT4.
How is data prepared for fine‑tuning?
Use the InstructionDataset class with Alpaca‑format JSON or plain text files.
Can I run models in a private VPC?
Yes, the library runs locally or in any private cloud environment.
What evaluation metrics are available?
Currently only perplexity is provided, with plans for additional metrics.
Project at a glance
Active- Stars
- 2,663
- Watchers
- 2,663
- Forks
- 207
LicenseApache-2.0
Repo age2 years old
Last commit3 weeks ago
Primary languagePython
Last synced yesterday