Instructions to use liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
Use Docker
docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liodon-ai/ReaderLM-v2-imatrix-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liodon-ai/ReaderLM-v2-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
- Ollama
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Ollama:
ollama run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
- Unsloth Studio
How to use liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-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 liodon-ai/ReaderLM-v2-imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for liodon-ai/ReaderLM-v2-imatrix-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Docker Model Runner:
docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
- Lemonade
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ReaderLM-v2-imatrix-GGUF-Q4_K_M
List all available models
lemonade list
Add iMatrix GGUF quantizations for ReaderLM-v2
Browse files- .gitattributes +7 -0
- README.md +64 -0
- ReaderLM-v2-IQ2_M.gguf +3 -0
- ReaderLM-v2-IQ3_M.gguf +3 -0
- ReaderLM-v2-IQ4_XS.gguf +3 -0
- ReaderLM-v2-Q4_K_M.gguf +3 -0
- ReaderLM-v2-Q5_K_M.gguf +3 -0
- ReaderLM-v2-Q6_K.gguf +3 -0
- ReaderLM-v2-Q8_0.gguf +3 -0
.gitattributes
CHANGED
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@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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ReaderLM-v2-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: other
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base_model: jinaai/ReaderLM-v2
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pipeline_tag: text-generation
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tags:
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- gguf
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- local-llm
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- llama.cpp
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- lm-studio
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- quantized
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- imatrix
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- sub-4-bit
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- qwen2
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---
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# ReaderLM-v2 — iMatrix GGUF
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GGUF quantizations of [jinaai/ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2), published by [Liodon AI](https://huggingface.co/liodon-ai).
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## Quick Start
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**llama.cpp**
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```bash
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llama-cli -hf liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
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```
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**Ollama**
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```bash
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ollama run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
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```
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**LM Studio / Jan** — search `liodon-ai/ReaderLM-v2-imatrix-GGUF` and pick your quant.
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## Quants
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| Quant | Size | VRAM est. | Notes |
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|-------|------|-----------|-------|
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| `IQ2_M` | 0.60 GB | ~1 GB | 2-bit, iMatrix — smallest usable |
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| `IQ3_M` | 0.78 GB | ~1 GB | 3-bit, iMatrix — great quality/size tradeoff |
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| `IQ4_XS` | 0.90 GB | ~1 GB | 4-bit extra-small, iMatrix |
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| `Q4_K_M` | 0.99 GB | ~1 GB | 4-bit, iMatrix-calibrated (recommended) |
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| `Q5_K_M` | 1.13 GB | ~1 GB | 5-bit, iMatrix-calibrated |
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| `Q6_K` | 1.27 GB | ~1 GB | 6-bit, iMatrix-calibrated, near-lossless |
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| `Q8_0` | 1.65 GB | ~2 GB | 8-bit, essentially lossless |
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## What is iMatrix?
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Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through
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the full-precision model to find which weights matter most, then allocates more precision where
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it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following —
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**same file size, better output**.
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Calibration: 2M tokens of [WikiText-103](https://huggingface.co/datasets/wikitext).
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> Also see plain (non-iMatrix) quants: `liodon-ai/ReaderLM-v2-GGUF`
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## Source
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- **Model**: [jinaai/ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2)
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- **License**: other
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---
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*Quantized by [Liodon AI](https://huggingface.co/liodon-ai)*
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ReaderLM-v2-IQ2_M.gguf
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size 601052672
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ReaderLM-v2-IQ3_M.gguf
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ReaderLM-v2-IQ4_XS.gguf
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ReaderLM-v2-Q4_K_M.gguf
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ReaderLM-v2-Q5_K_M.gguf
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ReaderLM-v2-Q6_K.gguf
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ReaderLM-v2-Q8_0.gguf
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