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

Libraries

How to use RayNene/Trilex with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RayNene/Trilex")
model = AutoModelForCausalLM.from_pretrained("RayNene/Trilex")
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 RayNene/Trilex with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RayNene/Trilex

SGLang

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

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

Trilex / adapter_config.json

RayNene

Upload folder using huggingface_hub

09740de verified about 19 hours ago

Raw

History Blame Contribute Delete

1.02 kB

	{
	"alpha_pattern": {},
	"auto_mapping": null,
	"base_model_name_or_path": "togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit",
	"bias": "none",
	"corda_config": null,
	"eva_config": null,
	"exclude_modules": [],
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 128,
	"lora_bias": false,
	"lora_dropout": 0.0,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"r": 64,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"feed_forward.gate_proj",
	"o_proj",
	"q_proj",
	"feed_forward.up_proj",
	"shared_expert.down_proj",
	"k_proj",
	"v_proj",
	"shared_expert.gate_proj",
	"shared_expert.up_proj",
	"feed_forward.down_proj"
	],
	"task_type": "CAUSAL_LM",
	"trainable_token_indices": null,
	"use_dora": false,
	"use_rslora": false
	}