Instructions to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m",
	filename="LFM2.5-VL-450M-methane-expert-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

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 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

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 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Use Docker

docker model run hf.co/5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

LM Studio
Jan
Ollama
How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with Ollama:
```
ollama run hf.co/5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
```

Unsloth Studio

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m 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 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m 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 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m to start chatting

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with Docker Model Runner:
```
docker model run hf.co/5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M
```

Lemonade

How to use 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m:Q4_K_M

Run and chat with the model

lemonade run user.lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m-Q4_K_M

List all available models

lemonade list

lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m / config.json

5ch4um1

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	{
	"architectures": [
	"Lfm2VlForConditionalGeneration"
	],
	"do_image_splitting": true,
	"downsample_factor": 2,
	"dtype": "bfloat16",
	"encoder_patch_size": 16,
	"image_token_id": 396,
	"max_image_tokens": 256,
	"max_pixels_tolerance": 2.0,
	"max_tiles": 10,
	"min_image_tokens": 64,
	"min_tiles": 2,
	"model_type": "lfm2_vl",
	"projector_bias": true,
	"projector_hidden_act": "gelu",
	"projector_hidden_size": 2048,
	"projector_use_layernorm": false,
	"text_config": {
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	"architectures": [
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	"conv_L_cache": 3,
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	"conv_use_xavier_init": true,
	"dtype": "bfloat16",
	"eos_token_id": 7,
	"full_attn_idxs": null,
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 6656,
	"layer_types": [
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	"vocab_size": 65536
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	"vision_config": {
	"attention_dropout": 0.0,
	"dtype": "bfloat16",
	"hidden_act": "gelu_pytorch_tanh",
	"hidden_size": 768,
	"intermediate_size": 3072,
	"layer_norm_eps": 1e-06,
	"model_type": "siglip2_vision_model",
	"num_attention_heads": 12,
	"num_channels": 3,
	"num_hidden_layers": 12,
	"num_patches": 256,
	"patch_size": 16,
	"vision_use_head": false
	}
	}