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.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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
- Atomic Chat new
- 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
File size: 2,926 Bytes
15c3607 | 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 | /**
* File upload lifecycle for the ChatScreen form.
*
* Owns the queue of processed `ChatUploadedFile`, the rejection-by-capability
* dialog state, and the dual-layer validation pipeline (general format +
* model modality). The caller provides the active model's capabilities and ID
* as reactive getters so validation tracks the model in real time.
*/
import { processFilesToChatUploaded } from '$lib/utils/browser-only';
import { isFileTypeSupported, filterFilesByModalities } from '$lib/utils';
interface UseChatScreenFileUploadOptions {
capabilities: () => { hasVision: boolean; hasAudio: boolean; hasVideo: boolean };
activeModelId: () => string | null | undefined;
}
export interface FileErrorData {
generallyUnsupported: File[];
modalityUnsupported: File[];
modalityReasons: Record<string, string>;
supportedTypes: string[];
}
export function useChatScreenFileUpload(options: UseChatScreenFileUploadOptions) {
let uploadedFiles = $state<ChatUploadedFile[]>([]);
let showFileErrorDialog = $state(false);
let fileErrorData = $state<FileErrorData>({
generallyUnsupported: [],
modalityUnsupported: [],
modalityReasons: {},
supportedTypes: []
});
async function processFiles(files: File[]) {
const generallySupported: File[] = [];
const generallyUnsupported: File[] = [];
for (const file of files) {
if (isFileTypeSupported(file.name, file.type)) {
generallySupported.push(file);
} else {
generallyUnsupported.push(file);
}
}
const { supportedFiles, unsupportedFiles, modalityReasons } = filterFilesByModalities(
generallySupported,
options.capabilities()
);
const allUnsupportedFiles = [...generallyUnsupported, ...unsupportedFiles];
if (allUnsupportedFiles.length > 0) {
const supportedTypes: string[] = ['text files', 'PDFs'];
const caps = options.capabilities();
if (caps.hasVision) supportedTypes.push('images');
if (caps.hasAudio) supportedTypes.push('audio files');
if (caps.hasVideo) supportedTypes.push('video files');
fileErrorData = {
generallyUnsupported,
modalityUnsupported: unsupportedFiles,
modalityReasons,
supportedTypes
};
showFileErrorDialog = true;
}
if (supportedFiles.length > 0) {
const processed = await processFilesToChatUploaded(
supportedFiles,
options.activeModelId() ?? undefined
);
uploadedFiles = [...uploadedFiles, ...processed];
}
}
function handleFileUpload(files: File[]) {
return processFiles(files);
}
function handleFileRemove(fileId: string) {
uploadedFiles = uploadedFiles.filter((f) => f.id !== fileId);
}
return {
get uploadedFiles() {
return uploadedFiles;
},
set uploadedFiles(value) {
uploadedFiles = value;
},
get showFileErrorDialog() {
return showFileErrorDialog;
},
set showFileErrorDialog(value) {
showFileErrorDialog = value;
},
fileErrorData,
handleFileUpload,
handleFileRemove
};
}
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