Instructions to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF", dtype="auto") - llama-cpp-python
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF", filename="opencoder-1.5b-instruct-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF: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 Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF: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 Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
- SGLang
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF 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 "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF" \ --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": "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF", "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 "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF" \ --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": "Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with Ollama:
ollama run hf.co/Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio new
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF 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 Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF 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 Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF to start chatting
- Docker Model Runner
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OpenCoder-1.5B-Instruct-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: other
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license_name: inf
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license_link: https://huggingface.co/infly/OpenCoder-1.5B-Instruct/blob/main/LICENSE
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language:
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- en
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- zh
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base_model: infly/OpenCoder-1.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- OpenCoder-LLM/opencoder-sft-stage1
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- OpenCoder-LLM/opencoder-sft-stage2
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tags:
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- llama-cpp
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- gguf-my-repo
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---
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# Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF
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This model was converted to GGUF format from [`infly/OpenCoder-1.5B-Instruct`](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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```bash
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brew install llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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### CLI:
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```bash
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llama-cli --hf-repo Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF --hf-file opencoder-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
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```
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### Server:
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```bash
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llama-server --hf-repo Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF --hf-file opencoder-1.5b-instruct-q4_k_m.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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Step 1: Clone llama.cpp from GitHub.
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```
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git clone https://github.com/ggerganov/llama.cpp
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```
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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```
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cd llama.cpp && LLAMA_CURL=1 make
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```
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Step 3: Run inference through the main binary.
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```
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./llama-cli --hf-repo Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF --hf-file opencoder-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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./llama-server --hf-repo Edge-Quant/OpenCoder-1.5B-Instruct-Q4_K_M-GGUF --hf-file opencoder-1.5b-instruct-q4_k_m.gguf -c 2048
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```
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