Instructions to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf", filename="reranker_0.5_bin_filt.IQ3_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with Ollama:
ollama run hf.co/RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
- Unsloth Studio new
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf to start chatting
- Pi new
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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": "RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-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 RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/lightblue_-_reranker_0.5_bin_filt-gguf:Q4_K_M
Run and chat with the model
lemonade run user.lightblue_-_reranker_0.5_bin_filt-gguf-Q4_K_M
List all available models
lemonade list
Quantization made by Richard Erkhov.
reranker_0.5_bin_filt - GGUF
- Model creator: https://huggingface.co/lightblue/
- Original model: https://huggingface.co/lightblue/reranker_0.5_bin_filt/
Original model description:
library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: reranker_binary_filt_train results: []
reranker_binary_filt_train
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the reranker_binary_filt_train dataset. It achieves the following results on the evaluation set:
- Loss: 0.0526
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0517 | 0.1000 | 1937 | 0.0871 |
| 0.114 | 0.2001 | 3874 | 0.0835 |
| 0.1033 | 0.3001 | 5811 | 0.0735 |
| 0.0544 | 0.4001 | 7748 | 0.0663 |
| 0.1169 | 0.5001 | 9685 | 0.0623 |
| 0.05 | 0.6002 | 11622 | 0.0599 |
| 0.0951 | 0.7002 | 13559 | 0.0566 |
| 0.0497 | 0.8002 | 15496 | 0.0551 |
| 0.1002 | 0.9002 | 17433 | 0.0532 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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