> ## Documentation Index > Fetch the complete documentation index at: https://liquidai-codex-model-library-starting-point.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # LFM2-8B-A1B > 8B parameter Mixture-of-Experts model with 1.5B active parameters (deprecated - use LFM2.5-8B-A1B instead) export const TextLlamacpp = ({ggufRepo, samplingFlags}) =>

Install:


{`brew install llama.cpp`.split('\n').map((line, i) => {line}{'\n'})}

Run:


{`llama-cli -hf ${ggufRepo} -c 4096 --color -i \\
    ${samplingFlags}`.split('\n').map((line, i) => {line}{'\n'})}

The -hf flag downloads the model directly from Hugging Face. For other installation methods and advanced usage, see the llama.cpp guide.

; export const TextSglang = ({modelId, toolCallParser, samplingParams}) =>

Install:


{`uv pip install "sglang>=0.5.10"`.split('\n').map((line, i) => {line}{'\n'})}

Launch server:


{(toolCallParser ? `sglang serve \\
    --model-path ${modelId} \\
    --host 0.0.0.0 \\
    --port 30000 \\
    --tool-call-parser ${toolCallParser}` : `sglang serve \\
    --model-path ${modelId} \\
    --host 0.0.0.0 \\
    --port 30000`).split('\n').map((line, i) => {line}{'\n'})}

Query (OpenAI-compatible):


{`from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="${modelId}",
    messages=[{"role": "user", "content": "What is machine learning?"}],
    ${samplingParams || "temperature=0.3,"}
)

print(response.choices[0].message.content)`.split('\n').map((line, i) => {line}{'\n'})}

; export const TextVllm = ({modelId, samplingParams, maxTokens}) =>

Install:


{`pip install vllm==0.14`.split('\n').map((line, i) => {line}{'\n'})}

Run:


{`from vllm import LLM, SamplingParams

llm = LLM(model="${modelId}")

sampling_params = SamplingParams(${samplingParams}max_tokens=${maxTokens || 512})

output = llm.chat("What is machine learning?", sampling_params)
print(output[0].outputs[0].text)`.split('\n').map((line, i) => {line}{'\n'})}

; export const TextTransformers = ({modelId, samplingParams}) =>

Install:


{`pip install "transformers>=5.2.0" torch accelerate`.split('\n').map((line, i) => {line}{'\n'})}

Download & Run:


{`from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "${modelId}"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is machine learning?"}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
    return_dict=True,
).to(model.device)

output = model.generate(**inputs, ${samplingParams}max_new_tokens=512)
input_length = inputs["input_ids"].shape[1]
response = tokenizer.decode(output[0][input_length:], skip_special_tokens=True)
print(response)`.split('\n').map((line, i) => {line}{'\n'})}

; ← Back to Text Models This model is deprecated. Use [LFM2.5-8B-A1B](/lfm/models/lfm25-8b-a1b) for improved performance and a 128K context length. LFM2-8B-A1B is Liquid AI's Mixture-of-Experts model, combining 8B total parameters with only 1.5B active parameters per forward pass. This delivers the quality of larger models with the speed and efficiency of smaller ones—ideal for on-device deployment.

HF GGUF MLX ONNX

## Specifications | Property | Value | | -------------- | ---------------- | | Parameters | 8B (1.5B active) | | Context Length | 32K tokens | | Architecture | LFM2 (MoE) |

8B quality, 1.5B inference cost Runs on phones and laptops Native function calling support

## Quick Start