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: 3,189 Bytes
c9c3262 | 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 | #pragma once
// clang-format off
#include "virtgpu-utils.h"
#include "virtgpu-shm.h"
#include "virtgpu-apir.h"
#include "backend/shared/api_remoting.h"
#include "backend/shared/apir_cs.h"
#include <fcntl.h>
#include <stdbool.h>
#include <stdio.h>
#include <sys/stat.h>
#include <sys/sysmacros.h>
#include <threads.h>
#include <xf86drm.h>
#include <cstring>
#define VIRGL_RENDERER_UNSTABLE_APIS 1
#include "apir_hw.h"
#include <drm/virtgpu_drm.h>
#include "venus_hw.h"
// clang-format on
#ifndef VIRTGPU_DRM_CAPSET_APIR
// Will be defined include/drm/virtgpu_drm.h when
// https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs
// is merged
# define VIRTGPU_DRM_CAPSET_APIR 10
#endif
// Mesa/Virlgrenderer Venus internal. Only necessary during the
// Venus->APIR transition in Virglrenderer
#define VENUS_COMMAND_TYPE_LENGTH 331
#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16
# define VIRTGPU_DRM_CAPSET_VENUS 4
#endif
typedef uint32_t virgl_renderer_capset;
/* from src/virtio/vulkan/vn_renderer_virtgpu.c */
#define VIRTGPU_PCI_VENDOR_ID 0x1af4
#define VIRTGPU_PCI_DEVICE_ID 0x1050
#define VIRTGPU_BLOB_MEM_GUEST_VRAM 0x0004
#define VIRTGPU_PARAM_GUEST_VRAM 9
#define SHMEM_DATA_SIZE 0x1830000 // 24MiB
#define SHMEM_REPLY_SIZE 0x4000
#define ARRAY_SIZE(x) (sizeof(x) / sizeof((x)[0]))
enum virt_gpu_result_t {
APIR_SUCCESS = 0,
APIR_ERROR_INITIALIZATION_FAILED = -1,
};
#define PRINTFLIKE(f, a) __attribute__((format(__printf__, f, a)))
struct virtgpu {
bool use_apir_capset;
int fd;
struct {
virgl_renderer_capset id;
uint32_t version;
virgl_renderer_capset_apir data;
} capset;
util_sparse_array shmem_array;
/* APIR communication pages */
virtgpu_shmem reply_shmem;
virtgpu_shmem data_shmem;
/* Mutex to protect shared data_shmem buffer from concurrent access */
mtx_t data_shmem_mutex;
/* Cached device information to prevent memory leaks and race conditions */
struct {
char * description;
char * name;
int32_t device_count;
uint32_t type;
size_t memory_free;
size_t memory_total;
} cached_device_info;
/* Cached buffer type information to prevent memory leaks and race conditions */
struct {
apir_buffer_type_host_handle_t host_handle;
char * name;
size_t alignment;
size_t max_size;
} cached_buffer_type;
};
static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) {
return drmIoctl(gpu->fd, request, args);
}
virtgpu * create_virtgpu();
apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags);
uint32_t remote_call(virtgpu * gpu,
apir_encoder * enc,
apir_decoder ** dec,
float max_wait_ms,
long long * call_duration_ns);
void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec);
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