Instructions to use oxbyte8/SparkBotLLM-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
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
- Pi
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"
File size: 3,146 Bytes
51ad3e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | {
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"selectedCheckpointStep": 128,
"selection": "Lowest domain validation loss; validation was used for checkpoint selection, not an independent final test.",
"trainingExamples": 242,
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"seed": 20261030,
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"learningRate": 5e-05,
"baseline": {
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"stoppedReason": null,
"seconds": 132.38,
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"published": false,
"limits": "Small synthetic pilot. Held-out loss and five routing checks do not establish broad factual accuracy or production quality."
} |