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

Libraries

How to use KirkAis/Lucent with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KirkAis/Lucent")
model = AutoModelForCausalLM.from_pretrained("KirkAis/Lucent", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KirkAis/Lucent 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 KirkAis/Lucent:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KirkAis/Lucent:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/KirkAis/Lucent:Q4_K_M

LM Studio
Jan

vLLM

How to use KirkAis/Lucent with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KirkAis/Lucent:Q4_K_M

SGLang

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

Ollama
How to use KirkAis/Lucent with Ollama:
```
ollama run hf.co/KirkAis/Lucent:Q4_K_M
```
Unsloth Desktop

How to use KirkAis/Lucent with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KirkAis/Lucent:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KirkAis/Lucent:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use KirkAis/Lucent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KirkAis/Lucent:Q4_K_M

Run and chat with the model

lemonade run user.Lucent-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KirkAis/Lucent with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KirkAis/Lucent: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 KirkAis/Lucent:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KirkAis/Lucent with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KirkAis/Lucent:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "KirkAis/Lucent:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

KirkAis commited on 10 days ago

Commit

829c5f3

verified ·

1 Parent(s): 9e4f01e

Update config.json architecture dimensions to 70B (72.7B) scale

Browse files

Files changed (1) hide show

config.json +28 -79

config.json CHANGED Viewed

@@ -1,81 +1,30 @@
 {
-    "architectures": [
-        "Qwen2ForCausalLM"
-    ],
-    "attention_dropout": 0.0,
-    "bos_token_id": 151643,
-    "torch_dtype": "bfloat16",
-    "eos_token_id": 151645,
-    "hidden_act": "silu",
-    "hidden_size": 5120,
-    "initializer_range": 0.02,
-    "intermediate_size": 13824,
-    "layer_types": [
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
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-        "full_attention",
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-        "full_attention",
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-        "full_attention",
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-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention"
-    ],
-    "max_position_embeddings": 32768,
-    "max_window_layers": 70,
-    "model_type": "qwen2",
-    "num_attention_heads": 40,
-    "num_hidden_layers": 48,
-    "num_key_value_heads": 8,
-    "pad_token_id": 151665,
-    "rms_norm_eps": 1e-06,
-    "rope_parameters": {
-        "rope_theta": 1000000.0,
-        "rope_type": "default"
-    },
-    "sliding_window": null,
-    "tie_word_embeddings": false,
-    "unsloth_fixed": true,
-    "use_cache": true,
-    "use_sliding_window": false,
-    "vocab_size": 152064
 }

 {
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 29568,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151665,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
 }