Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 6,820 Bytes
c305fa5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | # GGML-VirtGPU Backend Configuration
This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
## Environment Variables Overview
The ggml-virtgpu backend uses environment variables for configuration across three main components:
- **Frontend (Guest)**: GGML applications running in VMs
- **Hypervisor**: Virglrenderer/APIR system
- **Backend (Host)**: Host-side GGML backend integration
## Frontend (Guest-side) Configuration
### GGML_REMOTING_USE_APIR_CAPSET
- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
- **Type**: Boolean flag (presence-based)
- **Purpose**: Controls which virtio-gpu capability set to use for communication
- **Values**:
- Set (any value): Use the APIR capset (long-term setup)
- Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
- **Default**: Unset (Venus capset)
- **Usage**:
```bash
export GGML_REMOTING_USE_APIR_CAPSET=1 # Use APIR capset
# or leave unset for Venus capset
```
## Hypervisor (Virglrenderer/APIR) Configuration
These environment variables are used during the transition phase for
running with an unmodified hypervisor (not supporting the
VirglRenderer APIR component). They will be removed in the future, and
the hypervisor will instead configure VirglRenderer with the APIR
_Configuration Key_.
### VIRGL_APIR_BACKEND_LIBRARY
- **Location**: `virglrenderer/src/apir/apir-context.c`
- **Configuration Key**: `apir.load_library.path`
- **Type**: File path string
- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
- **Required**: Yes
- **Example**:
```bash
export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
```
### VIRGL_ROUTE_VENUS_TO_APIR
- **Location**: `virglrenderer/src/apir/apir-renderer.h`
- **Type**: Boolean flag (presence-based)
- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
- **Status**: will be removed once hypervisors support APIR natively
- **Warning**: Breaks normal Vulkan/Venus functionality
- **Usage**:
```bash
export VIRGL_ROUTE_VENUS_TO_APIR=1 # For testing with an unmodified hypervisor
```
### VIRGL_APIR_LOG_TO_FILE
- **Location**: `virglrenderer/src/apir/apir-renderer.c`
- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
- **Type**: File path string
- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
- **Required**: No (optional debugging)
- **Default**: Logging to `stderr`
- **Usage**:
```bash
export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
```
## Backend (Host-side) Configuration
These environment variables are used during the transition phase for
running with an unmodified hypervisor (not supporting the
VirglRenderer APIR component). They will be removed in the future, and
the hypervisor will instead configure VirglRenderer with the APIR
_Configuration Key_.
### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
- **Configuration Key**: `ggml.library.path`
- **Type**: File path string
- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
- **Required**: **Yes** - backend initialization fails without this
- **Examples**:
```bash
# macOS with Metal backend
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
# Linux with CUDA backend
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
# macOS or Linux with Vulkan backend
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
```
### APIR_LLAMA_CPP_GGML_LIBRARY_REG
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
- **Configuration Key**: `ggml.library.reg`
- **Type**: Function symbol name string
- **Purpose**: Name of the backend registration function to call after loading the library
- **Required**: No (defaults to `ggml_backend_init`)
- **Default**: `ggml_backend_init`
- **Examples**:
```bash
# Metal backend
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
# CUDA backend
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
# Vulkan backend
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
# Generic fallback (default)
# export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
```
### APIR_LLAMA_CPP_LOG_TO_FILE
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
- **Type**: File path string
- **Purpose**: Enable debug logging from the GGML backend to specified file
- **Required**: No (optional debugging)
- **Usage**:
```bash
export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
```
## Configuration Flow
The configuration system works as follows:
1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
- `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
- `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
- `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
- this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
3. **Backend Initialization**: The backend queries the configuration via callbacks:
- `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
- `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
## Error Messages
Common error scenarios and their messages:
- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
## Example Complete Configuration
Here's an example configuration for a macOS host with Metal backend:
```bash
# Hypervisor environment
export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
# Backend configuration
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
# Optional logging
export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
# Guest configuration
export GGML_REMOTING_USE_APIR_CAPSET=1
```
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