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
| import { describe, expect, it } from 'vitest'; | |
| import { | |
| getRequestUrl, | |
| getRequestMethod, | |
| getRequestBody, | |
| summarizeRequestBody, | |
| formatDiagnosticErrorMessage, | |
| extractJsonRpcMethods | |
| } from '$lib/utils/request-helpers'; | |
| describe('getRequestUrl', () => { | |
| it('returns a plain string input as-is', () => { | |
| expect(getRequestUrl('https://example.com/mcp')).toBe('https://example.com/mcp'); | |
| }); | |
| it('returns href from a URL object', () => { | |
| expect(getRequestUrl(new URL('https://example.com/mcp'))).toBe('https://example.com/mcp'); | |
| }); | |
| it('returns url from a Request object', () => { | |
| const req = new Request('https://example.com/mcp'); | |
| expect(getRequestUrl(req)).toBe('https://example.com/mcp'); | |
| }); | |
| }); | |
| describe('getRequestMethod', () => { | |
| it('prefers method from init', () => { | |
| expect(getRequestMethod('https://example.com', { method: 'POST' })).toBe('POST'); | |
| }); | |
| it('falls back to Request.method', () => { | |
| const req = new Request('https://example.com', { method: 'PUT' }); | |
| expect(getRequestMethod(req)).toBe('PUT'); | |
| }); | |
| it('falls back to baseInit.method', () => { | |
| expect(getRequestMethod('https://example.com', undefined, { method: 'DELETE' })).toBe('DELETE'); | |
| }); | |
| it('defaults to GET', () => { | |
| expect(getRequestMethod('https://example.com')).toBe('GET'); | |
| }); | |
| }); | |
| describe('getRequestBody', () => { | |
| it('returns body from init', () => { | |
| expect(getRequestBody('https://example.com', { body: 'payload' })).toBe('payload'); | |
| }); | |
| it('returns undefined when no body is present', () => { | |
| expect(getRequestBody('https://example.com')).toBeUndefined(); | |
| }); | |
| }); | |
| describe('summarizeRequestBody', () => { | |
| it('returns empty for null', () => { | |
| expect(summarizeRequestBody(null)).toEqual({ kind: 'empty' }); | |
| }); | |
| it('returns empty for undefined', () => { | |
| expect(summarizeRequestBody(undefined)).toEqual({ kind: 'empty' }); | |
| }); | |
| it('returns string kind with size', () => { | |
| expect(summarizeRequestBody('hello')).toEqual({ kind: 'string', size: 5 }); | |
| }); | |
| it('returns blob kind with size', () => { | |
| const blob = new Blob(['abc']); | |
| expect(summarizeRequestBody(blob)).toEqual({ kind: 'blob', size: 3 }); | |
| }); | |
| it('returns formdata kind', () => { | |
| expect(summarizeRequestBody(new FormData())).toEqual({ kind: 'formdata' }); | |
| }); | |
| it('returns arraybuffer kind with size', () => { | |
| expect(summarizeRequestBody(new ArrayBuffer(8))).toEqual({ kind: 'arraybuffer', size: 8 }); | |
| }); | |
| }); | |
| describe('formatDiagnosticErrorMessage', () => { | |
| it('appends CORS hint for Failed to fetch', () => { | |
| expect(formatDiagnosticErrorMessage(new TypeError('Failed to fetch'))).toBe( | |
| 'Failed to fetch (check CORS?)' | |
| ); | |
| }); | |
| it('passes through other error messages unchanged', () => { | |
| expect(formatDiagnosticErrorMessage(new Error('timeout'))).toBe('timeout'); | |
| }); | |
| it('handles non-Error values', () => { | |
| expect(formatDiagnosticErrorMessage('some string')).toBe('some string'); | |
| }); | |
| }); | |
| describe('extractJsonRpcMethods', () => { | |
| it('extracts methods from a JSON-RPC array', () => { | |
| const body = JSON.stringify([ | |
| { jsonrpc: '2.0', id: 1, method: 'initialize' }, | |
| { jsonrpc: '2.0', method: 'notifications/initialized' } | |
| ]); | |
| expect(extractJsonRpcMethods(body)).toEqual(['initialize', 'notifications/initialized']); | |
| }); | |
| it('extracts method from a single JSON-RPC message', () => { | |
| const body = JSON.stringify({ jsonrpc: '2.0', id: 1, method: 'tools/list' }); | |
| expect(extractJsonRpcMethods(body)).toEqual(['tools/list']); | |
| }); | |
| it('returns undefined for non-string body', () => { | |
| expect(extractJsonRpcMethods(null)).toBeUndefined(); | |
| expect(extractJsonRpcMethods(undefined)).toBeUndefined(); | |
| }); | |
| it('returns undefined for invalid JSON', () => { | |
| expect(extractJsonRpcMethods('not json')).toBeUndefined(); | |
| }); | |
| it('returns undefined when no methods found', () => { | |
| expect(extractJsonRpcMethods(JSON.stringify({ foo: 'bar' }))).toBeUndefined(); | |
| }); | |
| }); | |