Instructions to use mradermacher/OpenCoder-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/OpenCoder-8B-Instruct-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/OpenCoder-8B-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/OpenCoder-8B-Instruct-GGUF 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 mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
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 mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use mradermacher/OpenCoder-8B-Instruct-GGUF with Ollama:
ollama run hf.co/mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/OpenCoder-8B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/OpenCoder-8B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/OpenCoder-8B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OpenCoder-8B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
|
Download README.md from mradermacher/OpenCoder-8B-Instruct-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 3.68 kB
-
https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/README.md
- Command line
-
hf download hf://mradermacher/OpenCoder-8B-Instruct-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/README.md
3.68 kB
| base_model: infly/OpenCoder-8B-Instruct | |
| datasets: | |
| - OpenCoder-LLM/opencoder-sft-stage1 | |
| - OpenCoder-LLM/opencoder-sft-stage2 | |
| language: | |
| - en | |
| - zh | |
| library_name: transformers | |
| license: other | |
| license_link: https://huggingface.co/infly/OpenCoder-8B-Instruct/blob/main/LICENSE | |
| license_name: inf | |
| quantized_by: mradermacher | |
| ## About | |
| <!-- ### quantize_version: 2 --> | |
| <!-- ### output_tensor_quantised: 1 --> | |
| <!-- ### convert_type: hf --> | |
| <!-- ### vocab_type: --> | |
| <!-- ### tags: --> | |
| static quants of https://huggingface.co/infly/OpenCoder-8B-Instruct | |
| <!-- provided-files --> | |
| weighted/imatrix quants are available at https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-i1-GGUF | |
| ## Usage | |
| If you are unsure how to use GGUF files, refer to one of [TheBloke's | |
| READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for | |
| more details, including on how to concatenate multi-part files. | |
| ## Provided Quants | |
| (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | |
| | Link | Type | Size/GB | Notes | | |
| |:-----|:-----|--------:|:------| | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q2_K.gguf) | Q2_K | 3.1 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.3 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.6 | fast on arm, low quality | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.5 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.6 | | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q6_K.gguf) | Q6_K | 6.5 | very good quality | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.Q8_0.gguf) | Q8_0 | 8.4 | fast, best quality | | |
| | [GGUF](https://huggingface.co/mradermacher/OpenCoder-8B-Instruct-GGUF/resolve/main/OpenCoder-8B-Instruct.f16.gguf) | f16 | 15.6 | 16 bpw, overkill | | |
| Here is a handy graph by ikawrakow comparing some lower-quality quant | |
| types (lower is better): | |
|  | |
| And here are Artefact2's thoughts on the matter: | |
| https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 | |
| ## FAQ / Model Request | |
| See https://huggingface.co/mradermacher/model_requests for some answers to | |
| questions you might have and/or if you want some other model quantized. | |
| ## Thanks | |
| I thank my company, [nethype GmbH](https://www.nethype.de/), for letting | |
| me use its servers and providing upgrades to my workstation to enable | |
| this work in my free time. | |
| <!-- end --> | |