Instructions to use Petrouil/LFM2.5-Mosaic-900M-A500M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Petrouil/LFM2.5-Mosaic-900M-A500M-Base with Transformers:

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

pipe = pipeline("text-generation", model="Petrouil/LFM2.5-Mosaic-900M-A500M-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Petrouil/LFM2.5-Mosaic-900M-A500M-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Petrouil/LFM2.5-Mosaic-900M-A500M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Petrouil/LFM2.5-Mosaic-900M-A500M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Petrouil/LFM2.5-Mosaic-900M-A500M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Petrouil/LFM2.5-Mosaic-900M-A500M-Base

SGLang

How to use Petrouil/LFM2.5-Mosaic-900M-A500M-Base 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 "Petrouil/LFM2.5-Mosaic-900M-A500M-Base" \
    --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": "Petrouil/LFM2.5-Mosaic-900M-A500M-Base",
		"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 "Petrouil/LFM2.5-Mosaic-900M-A500M-Base" \
        --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": "Petrouil/LFM2.5-Mosaic-900M-A500M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Petrouil/LFM2.5-Mosaic-900M-A500M-Base with Docker Model Runner:
```
docker model run hf.co/Petrouil/LFM2.5-Mosaic-900M-A500M-Base
```

LFM2.5-Mosaic-900M-A500M-Base

Model Summary

LFM2.5-Mosaic-900M-A500M-Base is a Mixture-of-Experts (MoE) agentic assistant built on top of the frozen LFM2.5-350M-Base trunk. Ten MoE layers are inserted after the trunk's GQA attention blocks and late convolution blocks, giving the model a large learnable capacity while keeping the base trunk's pre-trained representations untouched.

Property	Value
Parameters (total)	921M
Parameters (active per token)	~543M
Parameters (trainable)	566M
MoE layers	10, inserted at blocks [2, 5, 8, 9, 10, 11, 12, 13, 14, 15]
Experts per layer	8 routed (top-2) + 1 shared, always-on
Expert shape	SwiGLU `1024 → 2048 → 1024` (~6.3M params each)
Context length	32,768
Vocabulary	65,536
Precision	bf16
Base trunk	frozen LFM2.5-350M-Base

The model is designed for agentic / tool-use workloads: it plans multi-turn tool-calling loops, emits structured tool calls, and follows instructions. It is not a general-knowledge or long-horizon reasoning model — use external tools for information retrieval, which is exactly what this model is built to orchestrate.

Uses

Intended use

Tool calling and function-calling in agentic workflows
Multi-turn task completion with tools (web search, lookups, form filling, etc.)
Plain chat and instruction-following in a tool-equipped assistant loop

Out of scope

General-knowledge mastery or factual breadth (use tools)
Heavy long-horizon reasoning
Code-heavy or specialized-domain expertise

How to Use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "petrouil/LFM2.5-Mosaic-900M-A500M-Base",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("petrouil/LFM2.5-Mosaic-900M-A500M-Base")

messages = [{"role": "user", "content": "Get the weather in Athens."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

Tool-call format

Training renders tool calls as JSON content in the assistant turn, e.g. [{"name": "get_weather", "arguments": {"city": "Athens"}}] with arguments as an object. The model may also emit the LFM2.5-native Pythonic form (<|tool_call_start|>get_weather<|tool_call_sep|>{"city": "Athens"}<|tool_call_end|>); both are accepted when the model is used with the LFM2.5 chat template.

Training Data

nvidia/Nemotron-SFT-Agentic-v2 (CC BY 4.0) — ~992K synthetic, LLM-judged tool-use trajectories: tool calling (707K), multi-turn customer service across 838 domains (279K), and web-search graph walks (6K). Subsampled to ~200M tokens (mix 70/25/5), streamed with byte-accurate Range-resume retries.
Instruct mix (~193M tokens) from HuggingFaceTB/smoltalk2 (Apache-2.0 / odc-by) and HuggingFaceH4/ultrachat_200k (MIT): everyday conversations, tulu-3 instruction-following, system chats, Magpie-style chat, OpenHermes, UltraChat, and more.
Curated persona cache — hand-written identity/social Q&A plus fluency slices, taught during the final touch-up phases.

Training Procedure

Frozen trunk: the entire LFM2.5-350M-Base backbone is frozen; only MoE routers, experts, and output scales are trained (the published checkpoint contains the full merged model).
Phases: short router-only warm-up with gumbel exploration, then joint router + expert training; gumbel decays to 0 over ~1K steps. Followed by continuation phases (agentic-only → instruct mix → curated persona touch-ups).
Optimizer: AdamW8bit; bf16 autocast; chunked CE over hidden states; grad clip 1.0; batch 98,304 tokens (micro-batch 1 × seq 2048 × 48 grad accum).
Balancing: sequence-wise load-balance loss (annealed over phases) + DeepSeek-style aux-loss-free per-expert bias (±0.001/step), keeping all experts used.
Hardware: single RTX 3070 (8 GB), ~6,300 tok/s steady state.

Limitations

Small model: weak general knowledge, limited long-horizon reasoning — rely on tools.
Routing: the router is domain-sensitive but soft (per-token entropy near its maximum); expect ensemble-flavored behavior rather than crisp per-domain expert switching.
Fluency: long creative generations can repeat — a mild repetition penalty at inference (repetition_penalty ≈ 1.1-1.2) is recommended.
License: custom license terms apply (see YAML metadata) — review before commercial redistribution.

Citation

@misc{mosaic2026,
  author = {Petrouil},
  title = {LFM2.5-Mosaic-900M-A500M-Base},
  year = {2026},
  url = {https://huggingface.co/petrouil/LFM2.5-Mosaic-900M-A500M-Base}
}

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Tensor type

F32

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