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 Desktop
- 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: 3,446 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 | import { describe, it, expect } from 'vitest';
import { LEGACY_AGENTIC_REGEX } from '$lib/constants/agentic';
/**
* Tests for legacy marker stripping (used in migration).
* The new system does not embed markers in content - these tests verify
* the legacy regex patterns still work for the migration code.
*/
// Mirror the legacy stripping logic used during migration
function stripLegacyContextMarkers(content: string): string {
return content
.replace(new RegExp(LEGACY_AGENTIC_REGEX.REASONING_BLOCK.source, 'g'), '')
.replace(LEGACY_AGENTIC_REGEX.REASONING_OPEN, '')
.replace(new RegExp(LEGACY_AGENTIC_REGEX.AGENTIC_TOOL_CALL_BLOCK.source, 'g'), '')
.replace(LEGACY_AGENTIC_REGEX.AGENTIC_TOOL_CALL_OPEN, '');
}
// A realistic complete tool call block as stored in old message.content
const COMPLETE_BLOCK =
'\n\n<<<AGENTIC_TOOL_CALL_START>>>\n' +
'<<<TOOL_NAME:bash_tool>>>\n' +
'<<<TOOL_ARGS_START>>>\n' +
'{"command":"ls /tmp","description":"list tmp"}\n' +
'<<<TOOL_ARGS_END>>>\n' +
'file1.txt\nfile2.txt\n' +
'<<<AGENTIC_TOOL_CALL_END>>>\n';
// Partial block: streaming was cut before END arrived.
const OPEN_BLOCK =
'\n\n<<<AGENTIC_TOOL_CALL_START>>>\n' +
'<<<TOOL_NAME:bash_tool>>>\n' +
'<<<TOOL_ARGS_START>>>\n' +
'{"command":"ls /tmp","description":"list tmp"}\n' +
'<<<TOOL_ARGS_END>>>\n' +
'partial output...';
describe('legacy agentic marker stripping (for migration)', () => {
it('strips a complete tool call block, leaving surrounding text', () => {
const input = 'Before.' + COMPLETE_BLOCK + 'After.';
const result = stripLegacyContextMarkers(input);
expect(result).not.toContain('<<<');
expect(result).toContain('Before.');
expect(result).toContain('After.');
});
it('strips multiple complete tool call blocks', () => {
const input = 'A' + COMPLETE_BLOCK + 'B' + COMPLETE_BLOCK + 'C';
const result = stripLegacyContextMarkers(input);
expect(result).not.toContain('<<<');
expect(result).toContain('A');
expect(result).toContain('B');
expect(result).toContain('C');
});
it('strips an open/partial tool call block (no END marker)', () => {
const input = 'Lead text.' + OPEN_BLOCK;
const result = stripLegacyContextMarkers(input);
expect(result).toBe('Lead text.');
expect(result).not.toContain('<<<');
});
it('does not alter content with no markers', () => {
const input = 'Just a normal assistant response.';
expect(stripLegacyContextMarkers(input)).toBe(input);
});
it('strips reasoning block independently', () => {
const input = '<<<reasoning_content_start>>>think hard<<<reasoning_content_end>>>Answer.';
expect(stripLegacyContextMarkers(input)).toBe('Answer.');
});
it('strips both reasoning and agentic blocks together', () => {
const input =
'<<<reasoning_content_start>>>plan<<<reasoning_content_end>>>' +
'Some text.' +
COMPLETE_BLOCK;
expect(stripLegacyContextMarkers(input)).not.toContain('<<<');
expect(stripLegacyContextMarkers(input)).toContain('Some text.');
});
it('empty string survives', () => {
expect(stripLegacyContextMarkers('')).toBe('');
});
it('detects legacy markers', () => {
expect(LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test('normal text')).toBe(false);
expect(
LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test('text<<<AGENTIC_TOOL_CALL_START>>>more')
).toBe(true);
expect(LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test('<<<reasoning_content_start>>>think')).toBe(
true
);
});
});
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