Instructions to use Laurence042/Meissa-Qwen2.5-7B-Instruct-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 Laurence042/Meissa-Qwen2.5-7B-Instruct-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 Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16 # Run inference directly in the terminal: llama cli -hf Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16 # Run inference directly in the terminal: llama cli -hf Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
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 Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
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 Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
Use Docker
docker model run hf.co/Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
- LM Studio
- Jan
- Ollama
How to use Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf with Ollama:
ollama run hf.co/Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
- Unsloth Desktop
- Docker Model Runner
How to use Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf with Docker Model Runner:
docker model run hf.co/Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
- Lemonade
How to use Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf:F16
Run and chat with the model
lemonade run user.Meissa-Qwen2.5-7B-Instruct-gguf-F16
List all available models
lemonade list
- Atomic Chat
Meissa-Qwen2.5-7B-Instruct-GGUF
本仓库提供 Orion-zhen/Meissa-Qwen2.5-7B-Instruct 的 GGUF 量化版本。
📢 声明 / Disclaimer
我只是一个搬运工。所有模型权重、微调功劳及相关权利均归原作者 Orion-zhen 所有。我进行 GGUF 转换和量化的目的是为了方便广大用户在 Ollama、llama.cpp 等推理框架中开箱即用,省去自行合并分卷及转换的麻烦。
🛠 量化细节 / Quantization Info
- 原始格式: Safetensors (Multi-shards)
- 转换工具:
llama.cpp - 量化精度:
Q4_K_M(推荐在 6GB-8GB 显存设备上运行) - 文件体积: 约 4.36 GB
🚀 快速使用 (Ollama)
你可以通过以下 Modelfile 快速导入:
- 下载
meissa-q4_k_m.gguf到本地。 - 创建一个名为
Modelfile的文件:
FROM "./meissa-q4_k_m.gguf"
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
- 执行命令:
ollama create meissa-7b -f Modelfile
⚖️ 许可说明
请遵循原模型的许可协议。
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Model tree for Laurence042/Meissa-Qwen2.5-7B-Instruct-gguf
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