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

Libraries

How to use regularpooria/Trix with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="regularpooria/Trix",
	filename="Trix-270M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use regularpooria/Trix with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf regularpooria/Trix
# Run inference directly in the terminal:
llama cli -hf regularpooria/Trix

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf regularpooria/Trix
# Run inference directly in the terminal:
llama cli -hf regularpooria/Trix

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf regularpooria/Trix
# Run inference directly in the terminal:
./llama-cli -hf regularpooria/Trix

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf regularpooria/Trix
# Run inference directly in the terminal:
./build/bin/llama-cli -hf regularpooria/Trix

Use Docker

docker model run hf.co/regularpooria/Trix

LM Studio
Jan

vLLM

How to use regularpooria/Trix with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/regularpooria/Trix

Ollama
How to use regularpooria/Trix with Ollama:
```
ollama run hf.co/regularpooria/Trix
```

Unsloth Studio

How to use regularpooria/Trix with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for regularpooria/Trix to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for regularpooria/Trix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for regularpooria/Trix to start chatting

Atomic Chat new
Docker Model Runner
How to use regularpooria/Trix with Docker Model Runner:
```
docker model run hf.co/regularpooria/Trix
```

Lemonade

How to use regularpooria/Trix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull regularpooria/Trix

Run and chat with the model

lemonade run user.Trix-{{QUANT_TAG}}

List all available models

lemonade list

Trix

File size: 1,352 Bytes

03c7f8d

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  "_sliding_window_pattern": 6,
  "architectures": [
    "Gemma3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_logit_softcapping": null,
  "bos_token_id": 2,
  "eos_token_id": 1,
  "final_logit_softcapping": null,
  "head_dim": 256,
  "hidden_activation": "gelu_pytorch_tanh",
  "hidden_size": 640,
  "initializer_range": 0.02,
  "intermediate_size": 2048,
  "layer_types": [
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    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "full_attention"
  ],
  "max_position_embeddings": 32768,
  "model_type": "gemma3_text",
  "num_attention_heads": 4,
  "num_hidden_layers": 18,
  "num_key_value_heads": 1,
  "pad_token_id": 0,
  "query_pre_attn_scalar": 256,
  "rms_norm_eps": 1e-06,
  "rope_local_base_freq": 10000.0,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "sliding_window": 512,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.55.0.dev0",
  "use_bidirectional_attention": false,
  "use_cache": true,
  "vocab_size": 262144
}