Instructions to use llama-swap/LFM3-1.7B-HelpAgent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llama-swap/LFM3-1.7B-HelpAgent with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="llama-swap/LFM3-1.7B-HelpAgent", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llama-swap/LFM3-1.7B-HelpAgent", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("llama-swap/LFM3-1.7B-HelpAgent", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Transformers.js

How to use llama-swap/LFM3-1.7B-HelpAgent with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'llama-swap/LFM3-1.7B-HelpAgent');

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llama-swap/LFM3-1.7B-HelpAgent with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Use Docker

docker model run hf.co/llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use llama-swap/LFM3-1.7B-HelpAgent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llama-swap/LFM3-1.7B-HelpAgent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llama-swap/LFM3-1.7B-HelpAgent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

SGLang

How to use llama-swap/LFM3-1.7B-HelpAgent with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "llama-swap/LFM3-1.7B-HelpAgent" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llama-swap/LFM3-1.7B-HelpAgent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "llama-swap/LFM3-1.7B-HelpAgent" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llama-swap/LFM3-1.7B-HelpAgent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use llama-swap/LFM3-1.7B-HelpAgent with Ollama:
```
ollama run hf.co/llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M
```
Unsloth Desktop

How to use llama-swap/LFM3-1.7B-HelpAgent with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use llama-swap/LFM3-1.7B-HelpAgent with Docker Model Runner:
```
docker model run hf.co/llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M
```

Lemonade

How to use llama-swap/LFM3-1.7B-HelpAgent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Run and chat with the model

lemonade run user.LFM3-1.7B-HelpAgent-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use llama-swap/LFM3-1.7B-HelpAgent with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use llama-swap/LFM3-1.7B-HelpAgent with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LFM3-1.7B-HelpAgent

A fine-tune of LFM3-1.7B that answers questions about llama-swap. It looks up the answer with llama-swap's documentation and config tools, and helps write config files.

It runs on llama-server, or entirely in the browser with Transformers.js and WebGPU. See Run it in the browser.

It is built on Liquid AI's LFM3-1.7B and is not made, affiliated with or endorsed by Liquid AI. See License.

Files

File	Size	Use
`LFM3-1.7B-HelpAgent-Q4_K_M.gguf`	1.1 GB	`llama-server`. This is the file the scores below measure.
`model.safetensors`	3.5 GB	bf16 weights for transformers or SGLang, or for making other quantizations.
`onnx/model_q4f16.onnx`	1.4 GB	Browsers with the `shader-f16` WebGPU feature.
`onnx/model_q4.onnx`	1.6 GB	Browsers without `shader-f16`.

Download the Q4_K_M GGUF, or from a terminal:

curl -LO https://huggingface.co/llama-swap/LFM3-1.7B-HelpAgent/resolve/main/LFM3-1.7B-HelpAgent-Q4_K_M.gguf

How it was evaluated

The Docs Agent eval in the llama-swap repository runs the real agent loop against real tools. Each question gets 3 attempts, and each attempt is graded by regex assertions. The training cases guided the work. The holdout cases were not trained on.

Q4_K_M on llama-server, with 60 seconds per attempt:

Model	Training cases (134)	Holdout cases (51)
LFM3-1.7B, stock	44.8%	55.6%
LFM3-1.7B-HelpAgent	95.5%	96.1%

An attempt took a median of 12 s and 32 to 34 s at the 95th percentile, on one RTX 3090 at concurrency 4. The stock model's low scores are mostly timeouts: 276 of its 555 attempts ran out of time, against 9 for this model.

The holdout score moved between 92.8% and 97.4% across runs with different repeat penalties and time limits, so treat small differences as noise.

Limitations

It only knows the llama-swap documentation it can reach through its tools, at the version it was trained on. It is not a general assistant.
It still invents config keys. An LLM judge flagged this in about 1 of 6 answers, which regex grading cannot see. Check generated config against the schema.
About 1.6% of attempts did not finish in 60 seconds, because the model reasoned for too long or repeated tool calls.
It has only been tested with llama-swap's system prompt and tools.

Usage

llama-server needs llama.cpp b11960 or later. These are the settings used for the scores above:

llama-server -hf llama-swap/LFM3-1.7B-HelpAgent:Q4_K_M --jinja \
  --ctx-size 131072 --parallel 4 --kv-unified \
  --temp 0.1 --top-k 50 --repeat-penalty 1.1

Requests need llama-swap's Docs Agent system prompt and the tools from llama-swap's /api/mcp endpoint. Tool calls use LFM3's XML format, which llama-server parses with --jinja.

With transformers, load the model with trust_remote_code=True. Read the included Python files first.

Run it in the browser

The ONNX models run with no server, using Transformers.js 4.3.2 and WebGPU. The browser downloads the model from this repository once and caches it. This needs a helper, because Docs Agent prompts are long:

import {
  AutoModelForCausalLM, AutoTokenizer, DynamicCache, Tensor, ones,
} from '@huggingface/transformers';

const id = 'llama-swap/LFM3-1.7B-HelpAgent';
const adapter = await navigator.gpu?.requestAdapter();
if (!adapter) throw new Error('WebGPU is not available');
const dtype = adapter.features.has('shader-f16') ? 'q4f16' : 'q4';

const tokenizer = await AutoTokenizer.from_pretrained(id);
const model = await AutoModelForCausalLM.from_pretrained(id, { dtype, device: 'webgpu' });

// A long prompt in one pass runs out of memory, so read it in chunks of 256 tokens.
async function prefill(ids, size = 256) {
  const cache = new DynamicCache();
  const total = ids.dims[1];
  for (let start = 0; start < total - 1; start += size) {
    const end = Math.min(start + size, total - 1);
    const output = await model({
      input_ids: ids.slice(null, [start, end]),
      attention_mask: ones([1, end]),
      past_key_values: cache,
      num_logits_to_keep: new Tensor('int64', [1n], []),
    });
    output.logits.dispose();
    cache.update(Object.fromEntries(
      Object.entries(output)
        .filter(([name]) => name.startsWith('present'))
        .map(([name, tensor]) => [
          name.replace('present_conv', 'past_conv').replace('present', 'past_key_values'),
          tensor,
        ]),
    ));
  }
  return cache;
}

// messages and tools come from your agent loop.
const prompt = tokenizer.apply_chat_template(messages, {
  tools, add_generation_prompt: true, tokenize: false,
});
const inputs = tokenizer(prompt, { add_special_tokens: false, return_tensors: 'pt' });
const cache = await prefill(inputs.input_ids);
const output = await model.generate({
  ...inputs, past_key_values: cache, max_new_tokens: 1024,
});
await cache.dispose();
const reply = tokenizer.batch_decode(
  output.slice(null, [inputs.input_ids.dims[1], null]),
  { skip_special_tokens: false },
)[0];

The reply holds raw <think> and <tool_call> text, so your agent loop has to parse it, run the tool and send the result back. generate() reads the sampling settings from generation_config.json.

Requirements and limits

Choose the file by shader-f16. q4f16 loads without it, then fails on the first generation with GatherBlockQuantized requires f16 but the device does not. Chrome 158 on Linux did not offer shader-f16 on an RTX 3090, so check for it rather than assuming a large GPU has it.
Always prefill in chunks. In one pass, q4 ran a 24 GB GPU out of memory at 7,767 tokens, and Liquid AI's notes report wrong output from q4f16 at 8,192. With chunks, memory stays low: q4f16 peaked at 3.2 GB of VRAM with a 14,170 token prompt.
Use Transformers.js 4.3.2. It is the only version tested.

Test results

On an RTX 3090, with seven real Docs Agent prompts of 3,622 to 14,170 tokens, and sampling off so runs can be compared:

	`q4f16`	`q4`
Runtime	Node with `onnxruntime-node`'s WebGPU, not a browser	Chromium 158 with WebGPU
Load from local disk	5 s	10 s
Prefill, 14,170 tokens	6 s	9 s
Generation	31 to 48 tokens/s	23 to 27 tokens/s
Prompts completed	7 of 7	7 of 7

Every prompt rendered byte-identically to Python, with the same token count. Chunked and single-pass logits matched closely: a mean difference of 0.02 on a range of about 30, with the same top 5 tokens, so chunks can change a word now and then but not the behavior. q4 and q4f16 wrote the same reply on 4 of the 7 prompts, and differed only in wording on the others.

Not tested:

q4f16 inside a real browser, because no browser here offered shader-f16. Test it on a machine that has the feature.
macOS, Windows, or any GPU other than an RTX 3090. Expect a smaller GPU to be slower.
The Docs Agent eval. The scores above are for the GGUF, not the ONNX models.

Changes from a standard export

onnx/ was made with Liquid AI's onnx-export at commit 26857c2, which writes logits_to_keep as an input, where Transformers.js sends num_logits_to_keep. These graphs take the latter, so lfm3-infer from that project will not run them. The ONNX weights were quantized from the same fine-tuned weights as the other files.

Training

Base: LFM3-1.7B, revision 0dc4268b561499c648c7ba7c9466bf3d8d01517a
Method: LoRA, then merged. r 16, alpha 32, dropout 0.05, learning rate 2e-4 with cosine decay, batch 1 accumulating 8, 2 epochs, bf16
Data: 417 conversations, 1,329 assistant turns. A stronger model, Q3.8-27B-rad, ran the real agent loop against llama-swap's documentation, and each conversation was filtered: it had to finish in few steps, and every config key in its answer had to appear in a tool result. No eval case was trained on.
Hardware: one RTX 3090, 87 minutes, 9 GB of VRAM

The work is tracked in llama-swap #1302.

License

This model is a derivative of LFM3-1.7B and is distributed under the LFM Open License v1.0. The license allows free commercial use only for organizations with annual revenue under $10 million USD. Above that, see Liquid AI's license guide. Research use by anyone, and non-commercial use by qualified nonprofits, is covered.

Modified files are listed in NOTICE. model.safetensors, the GGUF and the ONNX models contain modified weights. config.json and tokenizer_config.json have additions for Transformers.js. All other files are unchanged from LFM3-1.7B, except this model card.

The license grants no rights to Liquid AI's trademarks. "LFM3" appears in this model's name only to describe where it came from.

Citation

Please cite the base model:

@article{liquidAI2026LFM3,
  author  = {Liquid AI},
  title   = {Introducing LFM3: Every Modality for Specialized Intelligence},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm3},
}

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