Instructions to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-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 Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
# Run inference directly in the terminal:
llama cli -hf Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
# Run inference directly in the terminal:
llama cli -hf Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

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 Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

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 Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

Use Docker

docker model run hf.co/Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

LM Studio
Jan

vLLM

How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Soulfate24/ReaderLM-v2-ASHQ1-Remix-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": "Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

SGLang

How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-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 "Soulfate24/ReaderLM-v2-ASHQ1-Remix-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": "Soulfate24/ReaderLM-v2-ASHQ1-Remix-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 "Soulfate24/ReaderLM-v2-ASHQ1-Remix-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": "Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with Ollama:
```
ollama run hf.co/Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
```
Unsloth Desktop
Docker Model Runner
How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with Docker Model Runner:
```
docker model run hf.co/Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF
```

Lemonade

How to use Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

Run and chat with the model

lemonade run user.ReaderLM-v2-ASHQ1-Remix-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

ReaderLM-v2 - ASHQ1-Remix

This is a GGUF quantized version of the original model.

📈 Release Benchmarks (wiki.test.raw, symmetric FA-auto reference)

Tier	Size	PPL	KLD	RMS Δp	top-p
Fidelity-48pc	1422 MiB	17.0871	0.0050	1.65%	96.3%
Precision-42pc 🥈 Second Choice	1268 MiB	17.0608	0.0076	2.03%	95.5%
Quality-36pc ⭐ Recommended	1068 MiB	17.0610	0.0291	4.01%	91.2%
Compact-33pc	980 MiB	17.5504	0.1211	7.74%	87.1%
Mini-30pc	891 MiB	17.2444	0.1145	7.74%	85.4%
Nano-27pc	803 MiB	17.3237	0.1513	9.04%	81.4%
Pico-24pc	763 MiB	18.0892	0.2087	10.87%	77.9%

ℹ️ About ASHQ1-Remix Suite

Activation-aware GGUF quantization whose every ratio, floor, and cap traces to a measured experiment. Plain-BF16-native first; AutoRound lineage supported with explicit saturation bounds. Full seven-tier ladder validated across six model families.

🔗 Link: https://huggingface.co/Soulfate24/AutoRound-ASHQ1-Remix_Double-Quantization_Suite

Downloads last month: 750

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Soulfate24/ReaderLM-v2-ASHQ1-Remix-GGUF

Base model

jinaai/ReaderLM-v2

Quantized

(39)

this model