MODEL PROFILE

LFM2.5-1.2B-Thinking

LiquidAI · LFM2.5-1.2B-Thinking

LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming. 1. **Function definition**: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the `tokenizer.apply_chat_template()` function with tools. 2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between ` ` and ` ` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt. 3. **Function execution**: The function call is executed, and the result is returned as a "tool" role. 4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

Text generationlicense:other
Context window32,768 Token
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 17:34:10
OVERVIEW

Model overview

LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming. 1. **Function definition**: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the `tokenizer.apply_chat_template()` function with tools. 2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between ` ` and ` ` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt. 3. **Function execution**: The function call is executed, and the result is returned as a "tool" role. 4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。