Workflows/Build a local coding assistant with LFM2.5 2.6B

Coding / Intermediate

Build a local coding assistant with LFM2.5 2.6B

Set up LiquidAI/LFM2.5-2.6B via Transformers, verify it with a streaming generation, and connect it to a documented coding/agent client so it can complete a small code task.

EstimatedSource checked 9/25/2026

LOCALRENTED GPU

Before you begin

Difficulty: Intermediate
Software: Python, Transformers >= 5.0.0, PyTorch, Hermes (agent harness client)
Hardware: This setup uses Transformers. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.

CODING / TRANSFORMERSSOURCE-LINKED SETUP

Build a local coding assistant with LFM2.5 2.6B, step by step.

Set up LiquidAI/LFM2.5-2.6B via Transformers, verify it with a streaming generation, and connect it to a documented coding/agent client so it can complete a small code task.

Choose a package Run its commands Check the result

BEFORE YOU BEGIN

The documented package

This setup uses the package documented for this task. Review its source and supported platforms before starting.

The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.

CURRENT SETUP

LFM2.5 2.6B · official

This setup uses Transformers. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.

View weight source

THE WORKFLOW

Set up LFM2.5 2.6B on your machine

Pick your operating system. Every command below is for the selected package and runtime.

The publisher supplies Python code but does not specify an operating system. Linux is a practical starting environment, not a publisher-tested machine. Run the Python snippets in one session, in order.

Install the runtime prerequisites

You need Python with PyTorch installed. Transformers must be version 5.0.0 or newer to load LFM2.5. Confirm the version after installing or upgrading PyTorch and Transformers in your Python environment.

Generate with the exact model identifier

Save the following script as a Python file and run it in your activated environment. It downloads LiquidAI/LFM2.5-2.6B, streams the response, and prints output tokens as they are produced.

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-2.6B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
)["input_ids"].to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.1,
    max_new_tokens=512,
    streamer=streamer,
)

Point a documented agent client at the local model

Use the Hermes agent harness as the coding client. Configure a custom provider, the local OpenAI-compatible endpoint, the model name, context length, and tool use enforcement so it can act on code tasks.

hermes config set model.provider custom
hermes config set model.base_url http://localhost:8080/v1
hermes config set model.default LFM2.5-2.6B
hermes config set model.context_length 131072
hermes config set model.api_mode chat_completions
hermes config set agent.tool_use_enforcement true

Give the assistant a small code task

Start the Hermes session and ask it to make a specific, reviewable change in a small file — for example, request a short function with a clear signature, a docstring, and an edge-case check. Keep the prompt scoped so the diff is easy to inspect.

Check the generated result

Read the streamed output and the resulting file change. Confirm the assistant produced a complete, coherent answer that matches the requested task and that the code it produced is syntactically valid and addresses the requested edge case.

SUCCESS CHECK · LFM2.5 2.6B

Run a small code result

Ask for a self-contained example, run it in a disposable file, and inspect the code and output before using it in a real project.

This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.

Back to setup steps

When it doesn’t go to plan

Transformers cannot find or load the LFM2.5 architecture

Upgrade Transformers to 5.0.0 or newer in the same environment where PyTorch is installed, then run the generation script again.

The model runs out of memory or is extremely slow

Use device_map="auto" and dtype="bfloat16" as shown. If you have a compatible GPU, uncomment attn_implementation="flash_attention_2" in the script to speed up attention.

Hermes cannot reach the model (connection refused or 404)

Check that the OpenAI-compatible server is running on port 8080 and that model.base_url matches that port. Adjust the URL if you use a different backend port.

Hermes connects but does not call tools

Confirm agent.tool_use_enforcement is set to true and that model.api_mode is chat_completions, then start a new Hermes session.

REFERENCE LIBRARY

Sources and files

Original instructions, model files, and compatibility notes behind this setup.

2 SOURCES

01Publisher model card and weightshuggingface.co 02Publisher run instructionshuggingface.co

The model behind this workflow

LFM2.5 2.6B

MY HARDWARE

Will it run on your machine?

Save your machine to see a personalized rating and its reasoning.

Add my hardware

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