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: 2,833 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 | import { describe, it, expect } from 'vitest';
import { MessageRole } from '$lib/enums';
/**
* Tests for the new reasoning content handling.
* In the new architecture, reasoning content is stored in a dedicated
* `reasoningContent` field on DatabaseMessage, not embedded in content with tags.
* The API sends it as `reasoning_content` on ApiChatMessageData.
*/
describe('reasoning content in new structured format', () => {
it('reasoning is stored as separate field, not in content', () => {
// Simulate what the new chat store does
const message = {
content: 'The answer is 4.',
reasoningContent: 'Let me think: 2+2=4, basic arithmetic.'
};
// Content should be clean
expect(message.content).not.toContain('<<<');
expect(message.content).toBe('The answer is 4.');
// Reasoning in dedicated field
expect(message.reasoningContent).toBe('Let me think: 2+2=4, basic arithmetic.');
});
it('convertDbMessageToApiChatMessageData includes reasoning_content', () => {
// Simulate the conversion logic
const dbMessage = {
role: MessageRole.ASSISTANT,
content: 'The answer is 4.',
reasoningContent: 'Let me think: 2+2=4, basic arithmetic.'
};
const apiMessage: Record<string, unknown> = {
role: dbMessage.role,
content: dbMessage.content
};
if (dbMessage.reasoningContent) {
apiMessage.reasoning_content = dbMessage.reasoningContent;
}
expect(apiMessage.content).toBe('The answer is 4.');
expect(apiMessage.reasoning_content).toBe('Let me think: 2+2=4, basic arithmetic.');
// No internal tags leak into either field
expect(apiMessage.content).not.toContain('<<<');
expect(apiMessage.reasoning_content).not.toContain('<<<');
});
it('API message excludes reasoning when excludeReasoningFromContext is true', () => {
const dbMessage = {
role: MessageRole.ASSISTANT,
content: 'The answer is 4.',
reasoningContent: 'internal thinking'
};
const excludeReasoningFromContext = true;
const apiMessage: Record<string, unknown> = {
role: dbMessage.role,
content: dbMessage.content
};
if (!excludeReasoningFromContext && dbMessage.reasoningContent) {
apiMessage.reasoning_content = dbMessage.reasoningContent;
}
expect(apiMessage.content).toBe('The answer is 4.');
expect(apiMessage.reasoning_content).toBeUndefined();
});
it('handles messages with no reasoning', () => {
const dbMessage = {
role: MessageRole.ASSISTANT,
content: 'No reasoning here.',
reasoningContent: undefined
};
const apiMessage: Record<string, unknown> = {
role: dbMessage.role,
content: dbMessage.content
};
if (dbMessage.reasoningContent) {
apiMessage.reasoning_content = dbMessage.reasoningContent;
}
expect(apiMessage.content).toBe('No reasoning here.');
expect(apiMessage.reasoning_content).toBeUndefined();
});
});
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