Instructions to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="roleplaiapp/ReaderLM-v2-Q6_K-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("roleplaiapp/ReaderLM-v2-Q6_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use roleplaiapp/ReaderLM-v2-Q6_K-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 roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

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 roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

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 roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roleplaiapp/ReaderLM-v2-Q6_K-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": "roleplaiapp/ReaderLM-v2-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

SGLang

How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "roleplaiapp/ReaderLM-v2-Q6_K-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roleplaiapp/ReaderLM-v2-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "roleplaiapp/ReaderLM-v2-Q6_K-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roleplaiapp/ReaderLM-v2-Q6_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with Ollama:
```
ollama run hf.co/roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
```

Unsloth Studio

How to use roleplaiapp/ReaderLM-v2-Q6_K-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 roleplaiapp/ReaderLM-v2-Q6_K-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 roleplaiapp/ReaderLM-v2-Q6_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for roleplaiapp/ReaderLM-v2-Q6_K-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K
```

Lemonade

How to use roleplaiapp/ReaderLM-v2-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull roleplaiapp/ReaderLM-v2-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.ReaderLM-v2-Q6_K-GGUF-Q6_K

List all available models

lemonade list

roleplaiapp/ReaderLM-v2-Q6_K-GGUF

Repo: roleplaiapp/ReaderLM-v2-Q6_K-GGUF
Original Model: ReaderLM-v2 Organization: jinaai-v2 Quantized File: readerlm-v2-q6_k.gguf Quantization: GGUF Quantization Method: Q6_K
Use Imatrix: False
Split Model: False

Overview

This is an GGUF Q6_K quantized version of ReaderLM-v2.

Quantization By

I often have idle A100 GPUs while building/testing and training the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai

Downloads last month: 14

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

6-bit

Model tree for roleplaiapp/ReaderLM-v2-Q6_K-GGUF

Base model

jinaai/ReaderLM-v2

Quantized

(37)

this model