Instructions to use mav23/001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mav23/001 with Transformers:

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

pipe = pipeline("text-generation", model="mav23/001")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mav23/001")
model = AutoModelForCausalLM.from_pretrained("mav23/001", 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 mav23/001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mav23/001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mav23/001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mav23/001

SGLang

How to use mav23/001 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 "mav23/001" \
    --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": "mav23/001",
		"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 "mav23/001" \
        --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": "mav23/001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mav23/001 with Docker Model Runner:
```
docker model run hf.co/mav23/001
```

001 / reproduce /config.toml

mav23

Upload reproduce/config.toml with huggingface_hub

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	model = "LiquidAI/LFM2.5-230M"
	model_commit = "37b30cce3446f3f2e26a0d3f8c67c9167f5079d7"
	dtypes = [
	"auto",
	"float16",
	"bfloat16",
	"float32",
	]
	quantization = "none"
	device_map = "auto"
	offload_outputs_to_cpu = true
	batch_size = 128
	max_response_length = 100
	response_prefix = ""
	kl_divergence_scale = 1.0
	kl_divergence_target = 0.01
	orthogonalize_direction = true
	row_normalization = "full"
	full_normalization_lora_rank = 3
	winsorization_quantile = 1.0
	n_trials = 205
	n_startup_trials = 60
	seed = 71221607
	export_strategy = "merge"
	max_shard_size = "5GB"
	refusal_markers = [
	"disclaimer",
	"sorry",
	"i can'",
	"i cant",
	"i cannot",
	"i won'",
	"i wont",
	"i will not",
	"i unable",
	"im unable",
	"i'm unable",
	"i am unable",
	"i an ai",
	"im an ai",
	"i'm an ai",
	"i am an ai",
	"as an ai",
	"ai assistant",
	"i designed to",
	"im designed to",
	"i'm designed to",
	"i am designed to",
	"i programmed to",
	"im programmed to",
	"i'm programmed to",
	"i am programmed to",
	"violat",
	"prohibit",
	"illegal",
	"harmful",
	"inappropriate",
	"unethical",
	"ethical boundaries",
	]
	system_prompt = "You are a helpful assistant."

	[good_prompts]
	dataset = "mlabonne/harmless_alpaca"
	commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
	split = "train[:400]"
	column = "text"
	prefix = ""
	suffix = ""

	[bad_prompts]
	dataset = "mlabonne/harmful_behaviors"
	commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
	split = "train[:400]"
	column = "text"
	prefix = ""
	suffix = ""

	[good_evaluation_prompts]
	dataset = "mlabonne/harmless_alpaca"
	commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
	split = "test[:100]"
	column = "text"
	prefix = ""
	suffix = ""

	[bad_evaluation_prompts]
	dataset = "mlabonne/harmful_behaviors"
	commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
	split = "test[:100]"
	column = "text"
	prefix = ""
	suffix = ""