Instructions to use alwaysgood/TranslateQwen_v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use alwaysgood/TranslateQwen_v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="alwaysgood/TranslateQwen_v1-GGUF", filename="TranslateQwen_v1-BF16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use alwaysgood/TranslateQwen_v1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf alwaysgood/TranslateQwen_v1-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf alwaysgood/TranslateQwen_v1-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
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 alwaysgood/TranslateQwen_v1-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
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 alwaysgood/TranslateQwen_v1-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf alwaysgood/TranslateQwen_v1-GGUF:BF16
Use Docker
docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use alwaysgood/TranslateQwen_v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alwaysgood/TranslateQwen_v1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alwaysgood/TranslateQwen_v1-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
- Ollama
How to use alwaysgood/TranslateQwen_v1-GGUF with Ollama:
ollama run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
- Unsloth Studio
How to use alwaysgood/TranslateQwen_v1-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 alwaysgood/TranslateQwen_v1-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 alwaysgood/TranslateQwen_v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for alwaysgood/TranslateQwen_v1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use alwaysgood/TranslateQwen_v1-GGUF with Docker Model Runner:
docker model run hf.co/alwaysgood/TranslateQwen_v1-GGUF:BF16
- Lemonade
How to use alwaysgood/TranslateQwen_v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull alwaysgood/TranslateQwen_v1-GGUF:BF16
Run and chat with the model
lemonade run user.TranslateQwen_v1-GGUF-BF16
List all available models
lemonade list
TranslateQwen_v1 GGUF
GGUF conversion of alwaysgood/scp-stage4-oneshot-forward_dpo_sigmoid for llama.cpp and LM Studio.
Files
| File | Purpose |
|---|---|
TranslateQwen_v1-Q4_K_M.gguf |
Main text model, recommended default |
TranslateQwen_v1-BF16.gguf |
BF16 text model, larger reference build |
TranslateQwen_v1-mmproj-BF16.gguf |
Multimodal projector for vision/image input |
The main BF16 and Q4_K_M GGUF files are language/text models. Vision/image tensors are split into the separate mmproj-BF16 GGUF, so image input requires loading both the main model and the matching mmproj file.
LM Studio custom template
This model was trained with a raw DPO/SFT translation prompt, not a ChatML chat prompt. LM Studio's normal User/Assistant split can still be used by putting the fixed Instruction and Source headers in the custom template.
In LM Studio, open the model settings, go to Advanced Configuration -> Prompt Template, choose Custom, and use:
| Field | Value |
|---|---|
| Choose template | Custom |
| Before System | leave empty |
| After System | \n\n |
| Before User | ### Instruction:\nTranslate the English source into Korean.\n\n### Source:\n |
| After User | \n\n |
| Before Assistant | ### Response:\n |
| After Assistant | \n\n |
If you type this in LM Studio chat:
The Federal Reserve raised interest rates by 25 basis points.
LM Studio should send this prompt to the model:
### Instruction:
Translate the English source into Korean.
### Source:
The Federal Reserve raised interest rates by 25 basis points.
### Response:
Suggested decoding: greedy or low-temperature translation, e.g. temperature 0.0 to 0.3, context length 8192.
llama.cpp
Text:
llama-cli \
-m TranslateQwen_v1-Q4_K_M.gguf \
--color -ngl all -fa -c 8192 \
-p $'### Instruction:\nTranslate the English source into Korean.\n\n### Source:\nThe Federal Reserve raised interest rates by 25 basis points.\n\n### Response:\n'
Vision:
llama-mtmd-cli \
-m TranslateQwen_v1-Q4_K_M.gguf \
--mmproj TranslateQwen_v1-mmproj-BF16.gguf \
--image /path/to/image.png \
-p $'### Instruction:\nTranslate the English source into Korean.\n\n### Source:\nDescribe this image in Korean.\n\n### Response:\n' \
-ngl all -fa -c 8192
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