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 19

Commit

b2f9f9d

verified ·

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Update README.md

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README.md CHANGED Viewed

@@ -207,10 +207,10 @@ We report prefill throughput evaluated over a range of prompt lengths.
 | Platform / Device                                  | Inference | Framework        | Model              | 1K Prefill (tok/s) | 4K Prefill (tok/s) | 16K Prefill (tok/s) |  Memory |
 |----------------------------------------------------|-----------|------------------|-------------------|-------------------:|-------------------:|--------------------:| -------:|
-| AMD Ryzen™ AI 395+                                 | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,487              | 2,226          | 1,670           | 1.7 GB |
-| AMD Ryzen™ AI 9 HX 370                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,487              | 2,226          |1,670          | 1.7 GB |
-| AMD Ryzen™ AI 7 HX 350                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,431              | 2,032          |1,519          | 1.7 GB |
-| AMD Ryzen™ AI 5 HX 340                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,431              | 2,032          |1,519          | 1.7 GB |
 | AMD Ryzen™ AI 9 HX 370                              | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking| 2,975          | N/A                | N/A                | 856 MB |
 | Qualcomm Snapdragon® X Elite                       | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 2,591              | N/A                | N/A                 | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)        | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 4,391          | N/A                | N/A                 | 0.9 GB |
@@ -223,10 +223,10 @@ The reported results correspond to decoding 100 tokens at different context leng
 | Platform / Device                                  | Inference | Framework        | Model              | Decode @1K (tok/s) | Decode @4K (tok/s) | Decode @16K (tok/s) |  Memory |
 |----------------------------------------------------|-----------|------------------|-------------------|-------------------:|-------------------:|--------------------:| -------:|
-| AMD Ryzen™ AI 395+                                 | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 60              | 54          | 49           | 1.7 GB |
-| AMD Ryzen™ AI 9 HX 370                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 57              | 54          |49          | 1.7 GB |
-| AMD Ryzen™ AI 7 HX 350                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 63              | 59          |52          | 1.7 GB |
-| AMD Ryzen™ AI 5 HX 340                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 63              | 59          |52          | 1.7 GB |
 | AMD Ryzen™ AI 9 HX 370                              | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking| 116          | N/A                | N/A                | 856 MB |
 | Qualcomm Snapdragon® X Elite                       | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 63              | N/A                | N/A                 | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)        | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 82          | N/A                | N/A                 | 0.9 GB |

 | Platform / Device                                  | Inference | Framework        | Model              | 1K Prefill (tok/s) | 4K Prefill (tok/s) | 16K Prefill (tok/s) |  Memory |
 |----------------------------------------------------|-----------|------------------|-------------------|-------------------:|-------------------:|--------------------:| -------:|
+| AMD Ryzen™ AI 395+                                 | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,487              | 2,226          | 1,670           | 1.7 GB (full context) |
+| AMD Ryzen™ AI 9 HX 370                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,487              | 2,226          |1,670          | 1.7 GB (full context) |
+| AMD Ryzen™ AI 7 HX 350                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,431              | 2,032          |1,519          | 1.7 GB (full context) |
+| AMD Ryzen™ AI 5 HX 340                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 1,431              | 2,032          |1,519          | 1.7 GB (full context) |
 | AMD Ryzen™ AI 9 HX 370                              | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking| 2,975          | N/A                | N/A                | 856 MB |
 | Qualcomm Snapdragon® X Elite                       | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 2,591              | N/A                | N/A                 | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)        | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 4,391          | N/A                | N/A                 | 0.9 GB |
 | Platform / Device                                  | Inference | Framework        | Model              | Decode @1K (tok/s) | Decode @4K (tok/s) | Decode @16K (tok/s) |  Memory |
 |----------------------------------------------------|-----------|------------------|-------------------|-------------------:|-------------------:|--------------------:| -------:|
+| AMD Ryzen™ AI 395+                                 | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 60              | 54          | 49           | 1.7 GB (full context) |
+| AMD Ryzen™ AI 9 HX 370                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 57              | 54          |49          | 1.7 GB (full context) |
+| AMD Ryzen™ AI 7 HX 350                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 63              | 59          |52          | 1.7 GB (full context) |
+| AMD Ryzen™ AI 5 HX 340                              | NPU       | FastFlowLM       | LFM2.5-1.2B-Thinking| 63              | 59          |52          | 1.7 GB (full context) |
 | AMD Ryzen™ AI 9 HX 370                              | CPU       | llama.cpp (Q4_0) | LFM2.5-1.2B-Thinking| 116          | N/A                | N/A                | 856 MB |
 | Qualcomm Snapdragon® X Elite                       | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 63              | N/A                | N/A                 | 0.9 GB |
 | Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)        | NPU       | NexaML           | LFM2.5-1.2B-Thinking| 82          | N/A                | N/A                 | 0.9 GB |