Instructions to use oxbyte8/SparkBotLLM-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oxbyte8/SparkBotLLM-1.7B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM3-1.7B")
model = PeftModel.from_pretrained(base_model, "oxbyte8/SparkBotLLM-1.7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use oxbyte8/SparkBotLLM-1.7B 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 oxbyte8/SparkBotLLM-1.7B:Q4_0
# Run inference directly in the terminal:
llama cli -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf oxbyte8/SparkBotLLM-1.7B:Q4_0
# Run inference directly in the terminal:
llama cli -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf oxbyte8/SparkBotLLM-1.7B:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

Use Docker

docker model run hf.co/oxbyte8/SparkBotLLM-1.7B:Q4_0

Llama new
LM Studio
Jan

vLLM

How to use oxbyte8/SparkBotLLM-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oxbyte8/SparkBotLLM-1.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oxbyte8/SparkBotLLM-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oxbyte8/SparkBotLLM-1.7B:Q4_0

Ollama
How to use oxbyte8/SparkBotLLM-1.7B with Ollama:
```
ollama run hf.co/oxbyte8/SparkBotLLM-1.7B:Q4_0
```
Unsloth Desktop

How to use oxbyte8/SparkBotLLM-1.7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

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": "oxbyte8/SparkBotLLM-1.7B:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use oxbyte8/SparkBotLLM-1.7B with Docker Model Runner:
```
docker model run hf.co/oxbyte8/SparkBotLLM-1.7B:Q4_0
```

Lemonade

How to use oxbyte8/SparkBotLLM-1.7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oxbyte8/SparkBotLLM-1.7B:Q4_0

Run and chat with the model

lemonade run user.SparkBotLLM-1.7B-Q4_0

List all available models

lemonade list

Hermes Agent

How to use oxbyte8/SparkBotLLM-1.7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

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 oxbyte8/SparkBotLLM-1.7B:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use oxbyte8/SparkBotLLM-1.7B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxbyte8/SparkBotLLM-1.7B:Q4_0

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 "oxbyte8/SparkBotLLM-1.7B:Q4_0" \
  --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"

oxbyte8 commited on 3 days ago

Commit

cc77ef8

verified ·

1 Parent(s): f5d3a03

Fix adapter base-model metadata and pin the training revision

Browse files

Files changed (1) hide show

adapter_config.json +3 -3

adapter_config.json CHANGED Viewed

@@ -3,7 +3,7 @@
   "alpha_pattern": {},
   "arrow_config": null,
   "auto_mapping": null,
-  "base_model_name_or_path": null,
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,
@@ -30,7 +30,7 @@
   "qalora_group_size": 16,
   "r": 8,
   "rank_pattern": {},
-  "revision": null,
   "target_modules": [
     "in_proj",
     "k_proj",
@@ -51,4 +51,4 @@
   "use_qalora": false,
   "use_rslora": false,
   "velora_config": null
-}

   "alpha_pattern": {},
   "arrow_config": null,
   "auto_mapping": null,
+  "base_model_name_or_path": "LiquidAI/LFM3-1.7B",
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,
   "qalora_group_size": 16,
   "r": 8,
   "rank_pattern": {},
+  "revision": "ac2586a34f1a1c03329dbdcdefabd8b6478ddaa7",
   "target_modules": [
     "in_proj",
     "k_proj",
   "use_qalora": false,
   "use_rslora": false,
   "velora_config": null
+}