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: 2,834 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 | import { describe, expect, it } from 'vitest';
import { sanitizeHeaders } from '$lib/utils/api-headers';
import { CORS_PROXY_HEADER_PREFIX } from '$lib/constants';
describe('sanitizeHeaders', () => {
it('returns empty object for undefined input', () => {
expect(sanitizeHeaders()).toEqual({});
});
it('passes through non-sensitive headers', () => {
const headers = new Headers({ 'content-type': 'application/json', accept: 'text/html' });
expect(sanitizeHeaders(headers)).toEqual({
'content-type': 'application/json',
accept: 'text/html'
});
});
it('redacts known sensitive headers', () => {
const headers = new Headers({
authorization: 'Bearer secret',
'x-api-key': 'key-123',
'content-type': 'application/json'
});
const result = sanitizeHeaders(headers);
expect(result.authorization).toBe('[redacted]');
expect(result['x-api-key']).toBe('[redacted]');
expect(result['content-type']).toBe('application/json');
});
it('partially redacts headers specified in partialRedactHeaders', () => {
const headers = new Headers({ 'mcp-session-id': 'session-12345' });
const partial = new Map([['mcp-session-id', 5]]);
expect(sanitizeHeaders(headers, undefined, partial)['mcp-session-id']).toBe('....12345');
});
it('fully redacts mcp-session-id when no partialRedactHeaders is given', () => {
const headers = new Headers({ 'mcp-session-id': 'session-12345' });
expect(sanitizeHeaders(headers)['mcp-session-id']).toBe('[redacted]');
});
it('redacts extra headers provided by the caller', () => {
const headers = new Headers({
'x-vendor-key': 'vendor-secret',
'content-type': 'application/json'
});
const result = sanitizeHeaders(headers, ['x-vendor-key']);
expect(result['x-vendor-key']).toBe('[redacted]');
expect(result['content-type']).toBe('application/json');
});
it('handles case-insensitive extra header names', () => {
const headers = new Headers({ 'X-Custom-Token': 'token-value' });
const result = sanitizeHeaders(headers, ['X-CUSTOM-TOKEN']);
expect(result['x-custom-token']).toBe('[redacted]');
});
it('redacts proxied sensitive and custom target headers', () => {
const proxiedAuthorization = `${CORS_PROXY_HEADER_PREFIX}authorization`;
const proxiedSessionId = `${CORS_PROXY_HEADER_PREFIX}mcp-session-id`;
const proxiedVendorKey = `${CORS_PROXY_HEADER_PREFIX}x-vendor-key`;
const headers = new Headers({
[proxiedAuthorization]: 'Bearer secret',
[proxiedSessionId]: 'session-12345',
[proxiedVendorKey]: 'vendor-secret'
});
const partial = new Map([['mcp-session-id', 5]]);
const result = sanitizeHeaders(headers, ['x-vendor-key'], partial);
expect(result[proxiedAuthorization]).toBe('[redacted]');
expect(result[proxiedSessionId]).toBe('....12345');
expect(result[proxiedVendorKey]).toBe('[redacted]');
});
});
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