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

Libraries

How to use Cryptodk/Siru_SLM with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "Cryptodk/Siru_SLM")

Transformers

How to use Cryptodk/Siru_SLM with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Cryptodk/Siru_SLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cryptodk/Siru_SLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Cryptodk/Siru_SLM

SGLang

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

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

Siru_SLM / README.md

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	---
	base_model: meta-llama/Llama-3.1-8B-Instruct
	library_name: peft
	model_name: siru-dialogue-lora
	tags:
	- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
	- lora
	- sft
	- transformers
	- trl
	licence: license
	pipeline_tag: text-generation
	---

	# Model Card for siru-dialogue-lora

	This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Contents (export only)

	This folder keeps the final PEFT adapter plus tokenizer sidecars for inference. Intermediate `checkpoint-*` training snapshots are omitted to save disk; re-train if you need reproducible mid-run states.

	## Quick start (base + LoRA)

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel

	BASE = "meta-llama/Llama-3.1-8B-Instruct"
	ADAPTER = "." # or path / Hub id to this folder

	tokenizer = AutoTokenizer.from_pretrained(ADAPTER, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	BASE,
	torch_dtype=torch.float16,
	device_map="auto",
	trust_remote_code=True,
	)
	model = PeftModel.from_pretrained(model, ADAPTER)

	messages = [{"role": "user", "content": "Write one line of dialogue for a tense reunion."}]
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```

	## Training procedure





	This model was trained with SFT.

	### Framework versions

	- PEFT 0.19.1
	- TRL: 1.2.0
	- Transformers: 5.5.4
	- Pytorch: 2.11.0+cu126
	- Datasets: 4.8.4
	- Tokenizers: 0.22.2

	## Citations



	Cite TRL as:

	```bibtex
	@software{vonwerra2020trl,
	title = {{TRL: Transformers Reinforcement Learning}},
	author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
	license = {Apache-2.0},
	url = {https://github.com/huggingface/trl},
	year = {2020}
	}
	```