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

Libraries

How to use lthn/lemrd with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lthn/lemrd",
	filename="lemrd-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lthn/lemrd with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lthn/lemrd:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lthn/lemrd:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lthn/lemrd:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lthn/lemrd: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 lthn/lemrd:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lthn/lemrd: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 lthn/lemrd:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lthn/lemrd:Q4_K_M

Use Docker

docker model run hf.co/lthn/lemrd:Q4_K_M

LM Studio
Jan

vLLM

How to use lthn/lemrd with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lthn/lemrd"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lthn/lemrd",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lthn/lemrd:Q4_K_M

Ollama
How to use lthn/lemrd with Ollama:
```
ollama run hf.co/lthn/lemrd:Q4_K_M
```

Unsloth Studio new

How to use lthn/lemrd 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 lthn/lemrd 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 lthn/lemrd to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use lthn/lemrd with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lthn/lemrd: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": "lthn/lemrd:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lthn/lemrd with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lthn/lemrd: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 lthn/lemrd:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use lthn/lemrd with Docker Model Runner:
```
docker model run hf.co/lthn/lemrd:Q4_K_M
```

Lemonade

How to use lthn/lemrd with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lthn/lemrd:Q4_K_M

Run and chat with the model

lemonade run user.lemrd-Q4_K_M

List all available models

lemonade list

lthn commited on Apr 9

Commit

e6e303f

verified ·

1 Parent(s): f94870d

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +49 -11

config.json CHANGED Viewed

@@ -17,16 +17,6 @@
   "image_token_id": 258880,
   "initializer_range": 0.02,
   "model_type": "gemma4",
-  "quantization": {
-    "group_size": 64,
-    "bits": 4,
-    "mode": "affine"
-  },
-  "quantization_config": {
-    "group_size": 64,
-    "bits": 4,
-    "mode": "affine"
-  },
   "text_config": {
     "attention_bias": false,
     "attention_dropout": 0.0,
@@ -139,5 +129,53 @@
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0.dev0",
   "video_token_id": 258884,
-  "vision_soft_tokens_per_image": 280
 }

   "image_token_id": 258880,
   "initializer_range": 0.02,
   "model_type": "gemma4",
   "text_config": {
     "attention_bias": false,
     "attention_dropout": 0.0,
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0.dev0",
   "video_token_id": 258884,
+  "vision_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "chunk_size_feed_forward": 0,
+    "default_output_length": 280,
+    "dtype": "bfloat16",
+    "global_head_dim": 72,
+    "head_dim": 72,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_vision",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "num_key_value_heads": 16,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "position_embedding_size": 10240,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 100.0,
+      "rope_type": "default"
+    },
+    "standardize": true,
+    "use_clipped_linears": false
+  },
+  "vision_soft_tokens_per_image": 280,
+  "quantization_config": {
+    "bits": 4,
+    "group_size": 64,
+    "mode": "affine"
+  }
 }