Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-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 KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-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 KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
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 KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
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 KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF with Ollama:
ollama run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
- Lemonade
How to use KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KaLM-Embedding/KaLM-Reranker-V1-Large-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.KaLM-Reranker-V1-Large-Q8_0-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
Full FIQA Q8_0 evaluation
This report covers all 648 FIQA test queries, reranking the frozen top-100 retriever candidates for each query (64,800 scored pairs). All table rows use the same deterministic ranking policy.
| Model | NDCG@10 | MAP@10 | Recall@10 | MRR@10 | NDCG@100 |
|---|---|---|---|---|---|
| Retriever | 0.46985 | 0.38419 | 0.55188 | 0.54942 | 0.53769 |
| Transformers BF16 | 0.62486 | 0.54508 | 0.68604 | 0.70187 | 0.65898 |
| GGUF Q8_0 | 0.62444 | 0.54426 | 0.68654 | 0.70146 | 0.65835 |
| Check | Result |
|---|---|
| Q8_0 NDCG@10 drop vs BF16 | 0.00042 |
| Q8_0 MRR@10 drop vs BF16 | 0.00041 |
| Top-1 agreement with BF16 | 0.956790 |
| Mean top-10 set overlap | 0.961728 |
| Pairwise order consistency | 0.985426 |
| End-to-end throughput | 11.787 pairs/s |
| Completed shards | 81 |
| Full FIQA quality gate | accepted |
The Q8_0 release passes when both NDCG@10 and MRR@10 drops relative to Transformers BF16 are no greater than 0.005. This model passed both checks.
All 64,800 full-evaluation scores are finite, and all 81 shard states completed successfully.