Network · live0total tokens+102.17M

Confidential ortraditional. Your call.

Explore products Read docs

Trusted by builders & confidential-AI partners

Z.ai

Amazon

Z.ai

Amazon

~/projects/acme-api

Local

$llmtune chat
GLM-5.2 · streaming · /help for commands
>Explain this repo in plain English
scanning 4 files · building context...
>Refactor the auth flow
[GLM-5.2] working locally · no data uploaded

←GLM 5.2→◈Frontend↓⚡skill↑/commandGLM 5.2 model·Frontend agent·skill workflow·command prompt

Explore CLI

Agent Hub

AgentsAutonomous workers

SkillsReusable tool calls

CommandsTemplated prompt chains

Browse the full catalogAgents, skills, and commands — all composable.

Open Agent Hub

Products

One platform. Every layer of the stack.

Fine-tune, serve, and automate. Three products, built around the same confidential foundation.

Train & iterate

Fine-tune Studio

Your weights. Your rules.

Guided, no-code fine-tuning for any open model. Upload a dataset, pick a method, and ship a tuned model. SFT, DPO, KTO, LoRA, and QLoRA all supported.

SFT, DPO, KTO, LoRA, QLoRA
No-code guided workflows
Real-time training monitoring
Automatic checkpoints

Tune any open model

GPT-OSS

DeepSeek

Qwen

Llama+ more

Open Fine-tune Studio

Use cases

Built for serious AI workloads.

From domain assistants to multimodal models. One platform, organized around a single rule: your data stays yours.

01What teams create on the platform

Build / 01

Domain-aware assistants

Build secure, policy-controlled assistants trained on private data that never leaves your network. Deploy in hours.

One platform vs the silo

One platform, weighed against the vendor silo.

Scroll — or drag the divider. The left is how the market works today — siloed. The right is what the same market looks like with LLMTune: one open platform for every model, confidential fine-tuning, and a community you own.

Without LLMTune

One vendor, one account

Every model lives behind its own login. You juggle accounts just to compare them.

Locked into one method

Each platform forces a single fine-tune recipe. Want another? Switch vendors entirely.

Data held hostage

Confidential fine-tuning means uploading your corpora to shared vendor infra.

Priced like it's scarce

Per-token markups and reserved capacity billed at peak vendor rates.

Walled-garden skills

Agents, prompts, and skills locked inside each vendor’s silo.

With LLMTune

Every model, one account

Open or confidential, every vendor’s models — reached through a single LLMTune account.

Every method in one place

LoRA, QLoRA, DPO, full fine-tune — choose the recipe, not the vendor.

Confidential by default

Fine-tune any model. Your data stays encrypted, leak-proof, and yours.

Priced like it’s abundant

Near-free access on infrastructure that scales as the network grows.

A community you own

Open agent skills and prompts — built by the community, owned by no vendor.

Network · live0total tokens+102.17M

Confidential ortraditional. Your call.

Explore products Read docs

Trusted by builders & confidential-AI partners

Z.ai

Amazon

Z.ai

Amazon

~/projects/acme-api

Local

$llmtune chat
GLM-5.2 · streaming · /help for commands
>Explain this repo in plain English
scanning 4 files · building context...
>Refactor the auth flow
[GLM-5.2] working locally · no data uploaded

←GLM 5.2→◈Frontend↓⚡skill↑/commandGLM 5.2 model·Frontend agent·skill workflow·command prompt

Explore CLI

Agent Hub

AgentsAutonomous workers

SkillsReusable tool calls

CommandsTemplated prompt chains

Browse the full catalogAgents, skills, and commands — all composable.

Open Agent Hub

Products

One platform. Every layer of the stack.

Fine-tune, serve, and automate. Three products, built around the same confidential foundation.

Train & iterate

Fine-tune Studio

Your weights. Your rules.

Guided, no-code fine-tuning for any open model. Upload a dataset, pick a method, and ship a tuned model. SFT, DPO, KTO, LoRA, and QLoRA all supported.

SFT, DPO, KTO, LoRA, QLoRA
No-code guided workflows
Real-time training monitoring
Automatic checkpoints

Tune any open model

GPT-OSS

DeepSeek

Qwen

Llama+ more

Open Fine-tune Studio

Use cases

Built for serious AI workloads.

From domain assistants to multimodal models. One platform, organized around a single rule: your data stays yours.

01What teams create on the platform

Build / 01

Domain-aware assistants

Build secure, policy-controlled assistants trained on private data that never leaves your network. Deploy in hours.

One platform vs the silo

One platform, weighed against the vendor silo.

Without LLMTune

One vendor, one account

Every model lives behind its own login. You juggle accounts just to compare them.

Locked into one method

Each platform forces a single fine-tune recipe. Want another? Switch vendors entirely.

Data held hostage

Confidential fine-tuning means uploading your corpora to shared vendor infra.

Priced like it's scarce

Per-token markups and reserved capacity billed at peak vendor rates.

Walled-garden skills

Agents, prompts, and skills locked inside each vendor’s silo.

With LLMTune

Every model, one account

Open or confidential, every vendor’s models — reached through a single LLMTune account.

Every method in one place

LoRA, QLoRA, DPO, full fine-tune — choose the recipe, not the vendor.

Confidential by default

Fine-tune any model. Your data stays encrypted, leak-proof, and yours.

Priced like it’s abundant

Near-free access on infrastructure that scales as the network grows.

A community you own

Open agent skills and prompts — built by the community, owned by no vendor.