Use LiquidAI LFM2.5-2.6B with a signed-in browser

Yes. You can use the post-trained LiquidAI LFM2.5-2.6B with a signed-in browser when a local agent can run the exact weights, parse their tool calls, and invoke Tabbit CLI. Tabbit Browser holds the website session; Tabbit CLI gives the agent a separate task tab. There is an important catch: Liquid currently lists LFM2.5-2.6B as deprecated, so use it when the exact checkpoint matters and verify access before wiring it into a new project.

A user in r/LocalLLaMA described trying a custom harness on less than 2 GB of VRAM and said, “I just didn't think a model this small could possibly handle it.” That is one local test, not proof of browser support. The browser still depends on the agent host, its tool-call parser, and the Tabbit connection.

Key takeaways

• For this exact version, use the post-trained LiquidAI/LFM2.5-2.6B weights. Do not substitute the -Base checkpoint.

• Liquid's current model catalog marks LFM2.5-2.6B as deprecated, although the weights remain on Hugging Face.

• The LFM Open License limits commercial use for legal entities at or above USD 10 million in annual revenue.

• Pi can connect to a local OpenAI-compatible model endpoint. Pi skill discovery and Tabbit CLI launcher access are separate checks.

• Start with one read-only record. Compare the same stable ID, exact values, and source URLs before drafting any change.

Choose the route that actually serves this checkpoint

The official Liquid model card lists the post-trained 2.6B checkpoint for agentic workloads, with a separate base model for fine-tuning. It documents local deployment paths such as vLLM and SGLang, plus GGUF, MLX, and ONNX variants. Liquid's current model catalog now places LFM2.5-2.6B under deprecated models and lists newer text models above it. The weight repository is still public, but that does not guarantee a current API or model-picker route.

The model card describes LFM2.5-2.6B as a text-only model with a 131,072-token context window. It recommends tool use, data extraction, and agent tasks, but says not to use it for agentic coding or knowledge-heavy workloads. Treat the checkpoint as a candidate for short, bounded record lookups rather than a general browser researcher.

Also read the LFM Open License v1.0 before using or redistributing the weights commercially. The license ties commercial use to a USD 10 million annual-revenue threshold. The model card's “open-weight” label does not remove that condition.

Connect the model, Pi, and Tabbit in separate steps

Liquid's release post says to serve LFM2.5-2.6B behind an OpenAI-compatible endpoint and connect an agent harness. Its model card includes configuration examples for Pi, Hermes Agent, and OpenClaw. For this Tabbit workflow, Pi is a practical documented option because Pi also discovers global skills from ~/.agents/skills/ and invokes them as /skill:name.

1. Start with the exact model endpoint. Follow the model card's local inference guide and use the post-trained checkpoint, not the base. Check that a plain text prompt works first.

2. Check tool calling in the chosen host. LFM's card describes a Pythonic tool-call format. The host has to parse the model's output and run a tool. A text response or a raw call string does not prove the browser tool ran.

3. Sign in to the target site yourself in Tabbit Browser. Use the Tabbit profile that contains the account you intend to read. Do not put passwords, session tokens, or one-time codes in the prompt.

4. Install or discover the Tabbit skill in Pi. Invoke it as /skill:tabbit. Check that Pi can read the skill, then separately check that the local Tabbit CLI launcher is reachable. A visible skill description is not a successful CLI connection.

5. Open a harmless task page before the real record. The Tabbit CLI guide covers task tabs and commands. The agent connection guide explains the browser boundary; the first-task checklist is a good read-only preflight.

A basic CLI smoke test can check the browser connection without claiming that LFM has run a tool:

"$HOME/.local/bin/tabbit-cli" nodejs --task "LFM browser smoke test" --request-id lfm-smoke-001 <<'JS'
await page.goto("https://example.com/", {waitUntil: "domcontentloaded"});
return await tabbit.observe({frames: "none", maxChars: 2000});
JS

Use a new --request-id for each new program. Replace the public smoke-test page with an authorized test page only after you see the expected page title. If your host's tool trace is empty, stop and fix model/runtime parsing before judging the browser workflow. Compare browser MCP and CLI if you are deciding how the local agent should connect.

Compare two pages by one stable ID

Give the local agent a narrow read-only task. For example, a support dashboard and a separate account page might both show the same synthetic CASE-EXAMPLE-104. Ask the agent to match that exact key, not a name or a description that could match several records. Treat all sample IDs and output below as illustrative, not a Tabbit demo result.

If the key appears twice, the page shows a login screen, or a field is hidden, return the uncertainty and stop. Do not ask the model to “pick the most likely record.” For a larger read-only table, see browser data extraction to CSV; for a source-backed research report, see the browser research workflow.

Turn the read into a named draft for review

Ask the agent to return a report and save it as a clearly named draft such as LFM2.5-2.6B record check — CASE-EXAMPLE-104. Keep the source pages open in the separate Tabbit task tab while you review. The draft should show the same record ID, both exact field values, both URLs, and a short list of mismatches or unknowns. The checkpoint is visible: a person can compare each line with the two browser pages before copying it into a ticket or making a change.

Use the selected local LiquidAI LFM2.5-2.6B endpoint through this local agent. Use Tabbit CLI in my current Tabbit Browser environment. In a separate task tab, open [PAGE_A_URL] and [PAGE_B_URL]. Read only the record whose exact stable ID is [RECORD_ID]. Do not edit, submit, export, or change filters.

Return a Markdown draft named "LFM2.5-2.6B record check — [RECORD_ID]" with: the page heading and URL for each page; the exact ID; the requested fields [FIELD_1, FIELD_2]; a field-by-field match or mismatch; and any unavailable value marked unknown. Include the browser tool result for each page. If either page does not show exactly one matching ID, stop and report the ambiguity.

Expected output: a two-page comparison with source links, exact values, mismatches, unknowns, and “No write action taken.” This is an illustrative test workflow, not a completed model or production-site test.

Review the proposed draft against both visible pages. If the ID, value, or URL is wrong, correct the draft and rerun the read before any update. A separate task tab lets you keep working in your existing tabs; see Codex setup for another local host and the permissions guide for a separate permission checklist.

The parent team's Tabbit CLI fixture check found a 401 before test sign-in and read Release notes review plus GEO-104 in a second task after a short-lived test cookie was set. That supports session reuse between local CLI tasks in that fixture only. It does not show that Pi, LFM2.5-2.6B, or this two-page prompt was tested end to end.

When LFM2.5-2.6B is the wrong choice

Use this route only if your runtime can serve the precise checkpoint and the task stays small. The official model card says it is not recommended for agentic coding or knowledge-heavy work. Community reports are similarly narrow: one Reddit user said it located a deeply nested file through a custom harness, while another said it was useful for quick tasks but would choose a different model for important work. Those are individual experiences, not browser benchmarks.

A YouTube creator's local OpenCode test covers coding, reasoning, and tool calling. It is a client-side test of that workflow, not evidence of Tabbit compatibility. For higher-stakes or long-horizon browser work, use a model and host you have validated on the actual pages and permission boundaries.

If you do not specifically need the 2.6B checkpoint, start from a model Liquid currently lists. If you do need it, keep the weights, runtime, agent host, Tabbit skill, and site session as separate things to verify. Tabbit CLI setup explains the browser handoff, and browser-agent tutorials cover other read-only tasks.

Secholar.L

Growth & Content at Tabbit

Secholar.L writes hands-on guides about AI browsers and agent workflows, comparing tools with real research and citing verifiable sources.