Instructions to use FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 with Transformers:

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

pipe = pipeline("text-generation", model="FastFlowLM/LFM2.5-1.2B-Thinking-NPU2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FastFlowLM/LFM2.5-1.2B-Thinking-NPU2")
model = AutoModelForCausalLM.from_pretrained("FastFlowLM/LFM2.5-1.2B-Thinking-NPU2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FastFlowLM/LFM2.5-1.2B-Thinking-NPU2

SGLang

How to use FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 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 "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2" \
    --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": "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2",
		"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 "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2" \
        --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": "FastFlowLM/LFM2.5-1.2B-Thinking-NPU2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 with Docker Model Runner:
```
docker model run hf.co/FastFlowLM/LFM2.5-1.2B-Thinking-NPU2
```

FastFlowLM commited on Jan 18

Commit

a57e67c

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@@ -205,14 +205,20 @@ We report the following numbers with 1K prefill and 100 decode tokens:
 | Device                                               | Inference | Framework        | Model                | Prefill (tok/s) | Decode (tok/s) | Memory |
 | ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ------ |
-| AMD Ryzen AI 395+                                    | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1373            | 60             | 1700MB |
-| AMD Ryzen AI 9 HX 370                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1256            | 57             | 1700MB |
-| AMD Ryzen AI 9 HX 370                                | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 2975            | 116            | 856MB  |
 | Qualcomm Snapdragon® X Elite                         | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2591            | 63             | 0.9GB  |
 | Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro)           | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 4391            | 82             | 0.9GB  |
 | Qualcomm Dragonwing IQ9 (IQ-9075) (IoT)              | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2143            | 53             | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 335             | 70             | 719MB  |
 These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
 ## Contact

 | Device                                               | Inference | Framework        | Model                | Prefill (tok/s) | Decode (tok/s) | Memory |
 | ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ------ |
+| AMD Ryzen AI 395+                                    | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1487            | 60             | 1700MB |
+| AMD Ryzen AI 5 HX 340                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1431            | 63             | 1700MB |
+| AMD Ryzen AI 7 HX 350                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1431            | 63             | 1700MB |
+| AMD Ryzen AI 9 HX 370                                | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking | 1487            | 57             | 1700MB |
+| AMD Ryzen AI 9 HX 370                                | GPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 2975            | 116            | 856MB  |
 | Qualcomm Snapdragon® X Elite                         | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2591            | 63             | 0.9GB  |
 | Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro)           | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 4391            | 82             | 0.9GB  |
 | Qualcomm Dragonwing IQ9 (IQ-9075) (IoT)              | NPU       | NexaML           | LFM2.5-1.2B-Thinking | 2143            | 53             | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking | 335             | 70             | 719MB  |
+LFM2.5-1.2B-Thinking works very well with long contexts on AMD NPU. It delivers **~59 tok/s at 4K** context and **~52 tok/s at 16K** during decoding, and reaches a **prefill speed of ~2,226 tok/s with a 4K-token prompt**.
+Check the detailed benchmark results [here](https://fastflowlm.com/docs/benchmarks/lfm2_results/).
 These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
 ## Contact