Instructions to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF", dtype="auto") - llama-cpp-python
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF", filename="prophet-qwen3-4b-sft-q4_k_m.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Ollama:
ollama run hf.co/radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF to start chatting
- Pi
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
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 radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.prophet-qwen3-4b-sft-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
File size: 2,190 Bytes
1186a97 c656465 1186a97 4549b68 1186a97 | 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 | ---
base_model: radm/prophet-qwen3-4b-sft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
tags:
- qwen3
- sft
- unsloth
- philosophical
- esoteric
- llama-cpp
- gguf-my-repo
---
# radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF
<img src="https://huggingface.co/radm/prophet-qwen3-4b-sft/resolve/main/model-image.png" alt="Model Image" width="100%">
This model was converted to GGUF format from [`radm/prophet-qwen3-4b-sft`](https://huggingface.co/radm/prophet-qwen3-4b-sft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/radm/prophet-qwen3-4b-sft) for more details on the model.
## Usage
For chat templapte errors (eg lmstudio) use [this issue](https://github.com/lmstudio-ai/lmstudio-bug-tracker/issues/479#issuecomment-2701947624).
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF --hf-file prophet-qwen3-4b-sft-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF --hf-file prophet-qwen3-4b-sft-q4_k_m.gguf -c 2048
```
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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
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).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF --hf-file prophet-qwen3-4b-sft-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo radm/prophet-qwen3-4b-sft-Q4_K_M-GGUF --hf-file prophet-qwen3-4b-sft-q4_k_m.gguf -c 2048
```
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