Instructions to use John1604/DeepSeek-R1-0528-Qwen3-8B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use John1604/DeepSeek-R1-0528-Qwen3-8B-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 John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf John1604/DeepSeek-R1-0528-Qwen3-8B-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 John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf John1604/DeepSeek-R1-0528-Qwen3-8B-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 John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf John1604/DeepSeek-R1-0528-Qwen3-8B-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 John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M
Use Docker
docker model run hf.co/John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use John1604/DeepSeek-R1-0528-Qwen3-8B-gguf with Ollama:
ollama run hf.co/John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use John1604/DeepSeek-R1-0528-Qwen3-8B-gguf with Docker Model Runner:
docker model run hf.co/John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M
- Lemonade
How to use John1604/DeepSeek-R1-0528-Qwen3-8B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.DeepSeek-R1-0528-Qwen3-8B-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| license: apache-2.0 | |
| language: | |
| - en | |
| - zh | |
| base_model: | |
| - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B | |
| # Deepseek 8B 0528 | |
| This is the LLM about HIPPA law. Ask the LLM about HIPAA. | |
| ## Use the model in ollama | |
| ### First download and install ollama. | |
| https://ollama.com/download | |
| ### Command | |
| in windows command line, or in terminal in ubuntu, type: | |
| ``` | |
| ollama run hf.co/John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:q6_k | |
| ``` | |
| (q6_k is the model quant type, q5_k_s, q4_k_m, ..., can also be used) | |
| ``` | |
| C:\Users\developer>ollama run hf.co/John1604/DeepSeek-R1-0528-Qwen3-8B-gguf:q6_k | |
| pulling manifest | |
| ... | |
| verifying sha256 digest | |
| writing manifest | |
| success | |
| >>> Send a message (/? for help) | |
| ``` | |
| ## Use the model in LM Studio | |
| ### download and install LM Studio | |
| https://lmstudio.ai/ | |
| ## Discover models | |
| ### In the LM Studio, click "Discover" icon. "Mission Control" popup window will be displayed. | |
| ### In the "Mission Control" search bar, type "John1604/DeepSeek-R1-0528-Qwen3-8B-gguf" and check "GGUF", the model should be found. | |
| ### Download the model. | |
| ### Load the model. | |
| ### Ask questions. | |
| ## quantized models | |
| | Type | Bits | Quality | Description | | |
| | ---------- | ----- | ---------------- | ------------------------------------ | | |
| | **Q2_K** | 2-bit | 🟥 Low | Minimal footprint; only for tests | | |
| | **Q3_K_S** | 3-bit | 🟧 Low | “Small” variant (less accurate) | | |
| | **Q3_K_M** | 3-bit | 🟧 Low–Med | “Medium” variant | | |
| | **Q4_K_S** | 4-bit | 🟨 Med | Small, faster, slightly less quality | | |
| | **Q4_K_M** | 4-bit | 🟩 Med–High | “Medium” — best 4-bit balance | | |
| | **Q5_K_S** | 5-bit | 🟩 High | Slightly smaller than Q5_K_M | | |
| | **Q5_K_M** | 5-bit | 🟩🟩 High | Excellent general-purpose quant | | |
| | **Q6_K** | 6-bit | 🟩🟩🟩 Very High | Almost FP16 quality, larger size | | |
| | **Q8_0** | 8-bit | 🟩🟩🟩🟩 | Near-lossless baseline | | |