Instructions to use mkurman/LFM2.5-2.6B-MedIT-Mesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkurman/LFM2.5-2.6B-MedIT-Mesh with Transformers:

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

pipe = pipeline("text-generation", model="mkurman/LFM2.5-2.6B-MedIT-Mesh")
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("mkurman/LFM2.5-2.6B-MedIT-Mesh")
model = AutoModelForCausalLM.from_pretrained("mkurman/LFM2.5-2.6B-MedIT-Mesh", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mkurman/LFM2.5-2.6B-MedIT-Mesh with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mkurman/LFM2.5-2.6B-MedIT-Mesh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkurman/LFM2.5-2.6B-MedIT-Mesh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mkurman/LFM2.5-2.6B-MedIT-Mesh

SGLang

How to use mkurman/LFM2.5-2.6B-MedIT-Mesh 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 "mkurman/LFM2.5-2.6B-MedIT-Mesh" \
    --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": "mkurman/LFM2.5-2.6B-MedIT-Mesh",
		"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 "mkurman/LFM2.5-2.6B-MedIT-Mesh" \
        --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": "mkurman/LFM2.5-2.6B-MedIT-Mesh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mkurman/LFM2.5-2.6B-MedIT-Mesh with Docker Model Runner:
```
docker model run hf.co/mkurman/LFM2.5-2.6B-MedIT-Mesh
```

mkurman commited on Aug 7

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79ea9c2

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@@ -28,7 +28,7 @@ tags:
 base_model: LiquidAI/LFM2.5-2.6B
 ---
-This is an effect of an experimental technique called "MedIT Mesh", which is a method for improving the performance of large language models without the data and compute requirements of traditional fine-tuning, by looking only at the model's weights.
 I evaluated it on GPQA-diamond +0.015 (0.485 vs 0.470) and IFBench +0.023 (0.620 vs 0.597).

 base_model: LiquidAI/LFM2.5-2.6B
 ---
+This is the result of an experimental technique called "MedIT Mesh", which is a method for improving the performance of large language models without the data and compute requirements of traditional fine-tuning, by looking only at the model's weights.
 I evaluated it on GPQA-diamond +0.015 (0.485 vs 0.470) and IFBench +0.023 (0.620 vs 0.597).