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 { ModelsService } from '$lib/services/models.service'; | |
| const { parseModelId } = ModelsService; | |
| describe('parseModelId', () => { | |
| it('handles unknown patterns correctly', () => { | |
| expect(parseModelId('model-name-1')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'model-name-1', | |
| orgName: null, | |
| params: null, | |
| quantization: null, | |
| raw: 'model-name-1', | |
| tags: [] | |
| }); | |
| expect(parseModelId('org/model-name-2')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'model-name-2', | |
| orgName: 'org', | |
| params: null, | |
| quantization: null, | |
| raw: 'org/model-name-2', | |
| tags: [] | |
| }); | |
| }); | |
| it('extracts model parameters correctly', () => { | |
| expect(parseModelId('model-100B-BF16')).toMatchObject({ params: '100B' }); | |
| expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ params: '100B' }); | |
| }); | |
| it('extracts model parameters correctly in lowercase', () => { | |
| expect(parseModelId('model-100b-bf16')).toMatchObject({ params: '100B' }); | |
| expect(parseModelId('model-100b:q4_k_m')).toMatchObject({ params: '100B' }); | |
| }); | |
| it('extracts activated parameters correctly', () => { | |
| expect(parseModelId('model-100B-A10B-BF16')).toMatchObject({ activatedParams: 'A10B' }); | |
| expect(parseModelId('model-100B-A10B:Q4_K_M')).toMatchObject({ activatedParams: 'A10B' }); | |
| }); | |
| it('extracts activated parameters correctly in lowercase', () => { | |
| expect(parseModelId('model-100b-a10b-bf16')).toMatchObject({ activatedParams: 'A10B' }); | |
| expect(parseModelId('model-100b-a10b:q4_k_m')).toMatchObject({ activatedParams: 'A10B' }); | |
| }); | |
| it('extracts quantization correctly', () => { | |
| // Dash-separated quantization | |
| expect(parseModelId('model-100B-UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' }); | |
| expect(parseModelId('model-100B-IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' }); | |
| expect(parseModelId('model-100B-Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' }); | |
| expect(parseModelId('model-100B-Q8_0')).toMatchObject({ quantization: 'Q8_0' }); | |
| expect(parseModelId('model-100B-UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' }); | |
| expect(parseModelId('model-100B-F16')).toMatchObject({ quantization: 'F16' }); | |
| expect(parseModelId('model-100B-BF16')).toMatchObject({ quantization: 'BF16' }); | |
| expect(parseModelId('model-100B-MXFP4')).toMatchObject({ quantization: 'MXFP4' }); | |
| // Colon-separated quantization | |
| expect(parseModelId('model-100B:UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' }); | |
| expect(parseModelId('model-100B:IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' }); | |
| expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' }); | |
| expect(parseModelId('model-100B:Q8_0')).toMatchObject({ quantization: 'Q8_0' }); | |
| expect(parseModelId('model-100B:UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' }); | |
| expect(parseModelId('model-100B:F16')).toMatchObject({ quantization: 'F16' }); | |
| expect(parseModelId('model-100B:BF16')).toMatchObject({ quantization: 'BF16' }); | |
| expect(parseModelId('model-100B:MXFP4')).toMatchObject({ quantization: 'MXFP4' }); | |
| // Dot-separated quantization | |
| expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toMatchObject({ | |
| quantization: 'Q4_K_M' | |
| }); | |
| }); | |
| it('extracts additional tags correctly', () => { | |
| expect(parseModelId('model-100B-foobar-Q4_K_M')).toMatchObject({ tags: ['foobar'] }); | |
| expect(parseModelId('model-100B-A10B-foobar-1M-BF16')).toMatchObject({ | |
| tags: ['foobar', '1M'] | |
| }); | |
| expect(parseModelId('model-100B-1M-foobar:UD-Q8_K_XL')).toMatchObject({ | |
| tags: ['1M', 'foobar'] | |
| }); | |
| }); | |
| it('filters out container format segments from tags', () => { | |
| expect(parseModelId('model-100B-GGUF-Instruct-BF16')).toMatchObject({ | |
| tags: ['Instruct'] | |
| }); | |
| expect(parseModelId('model-100B-GGML-Instruct:Q4_K_M')).toMatchObject({ | |
| tags: ['Instruct'] | |
| }); | |
| }); | |
| it('handles real-world examples correctly', () => { | |
| expect(parseModelId('meta-llama/Llama-3.1-8B')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'Llama-3.1', | |
| orgName: 'meta-llama', | |
| params: '8B', | |
| quantization: null, | |
| raw: 'meta-llama/Llama-3.1-8B', | |
| tags: [] | |
| }); | |
| expect(parseModelId('openai/gpt-oss-120b-MXFP4')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'gpt-oss', | |
| orgName: 'openai', | |
| params: '120B', | |
| quantization: 'MXFP4', | |
| raw: 'openai/gpt-oss-120b-MXFP4', | |
| tags: [] | |
| }); | |
| expect(parseModelId('openai/gpt-oss-20b:Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'gpt-oss', | |
| orgName: 'openai', | |
| params: '20B', | |
| quantization: 'Q4_K_M', | |
| raw: 'openai/gpt-oss-20b:Q4_K_M', | |
| tags: [] | |
| }); | |
| expect(parseModelId('Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16')).toStrictEqual({ | |
| activatedParams: 'A3B', | |
| modelName: 'Qwen3-Coder', | |
| orgName: 'Qwen', | |
| params: '30B', | |
| quantization: 'BF16', | |
| raw: 'Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16', | |
| tags: ['Instruct', '1M'] | |
| }); | |
| }); | |
| it('handles real-world examples with quantization in segments', () => { | |
| expect(parseModelId('meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'Llama-4-Scout', | |
| orgName: 'meta-llama', | |
| params: '17B', | |
| quantization: 'Q4_K_M', | |
| raw: 'meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M', | |
| tags: ['16E', 'Instruct'] | |
| }); | |
| expect(parseModelId('MiniMaxAI/MiniMax-M2-IQ4_XS')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'MiniMax-M2', | |
| orgName: 'MiniMaxAI', | |
| params: null, | |
| quantization: 'IQ4_XS', | |
| raw: 'MiniMaxAI/MiniMax-M2-IQ4_XS', | |
| tags: [] | |
| }); | |
| expect(parseModelId('MiniMaxAI/MiniMax-M2-UD-Q3_K_XL')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'MiniMax-M2', | |
| orgName: 'MiniMaxAI', | |
| params: null, | |
| quantization: 'UD-Q3_K_XL', | |
| raw: 'MiniMaxAI/MiniMax-M2-UD-Q3_K_XL', | |
| tags: [] | |
| }); | |
| expect(parseModelId('mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'Devstral-2', | |
| orgName: 'mistralai', | |
| params: '123B', | |
| quantization: 'Q4_K_M', | |
| raw: 'mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M', | |
| tags: ['Instruct', '2512'] | |
| }); | |
| expect(parseModelId('mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'Devstral-Small-2', | |
| orgName: 'mistralai', | |
| params: '24B', | |
| quantization: 'Q8_0', | |
| raw: 'mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0', | |
| tags: ['Instruct', '2512'] | |
| }); | |
| expect(parseModelId('noctrex/GLM-4.7-Flash-MXFP4_MOE')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'GLM-4.7-Flash', | |
| orgName: 'noctrex', | |
| params: null, | |
| quantization: 'MXFP4_MOE', | |
| raw: 'noctrex/GLM-4.7-Flash-MXFP4_MOE', | |
| tags: [] | |
| }); | |
| expect(parseModelId('Qwen/Qwen3-Coder-Next-Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'Qwen3-Coder-Next', | |
| orgName: 'Qwen', | |
| params: null, | |
| quantization: 'Q4_K_M', | |
| raw: 'Qwen/Qwen3-Coder-Next-Q4_K_M', | |
| tags: [] | |
| }); | |
| expect(parseModelId('openai/gpt-oss-120b-Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'gpt-oss', | |
| orgName: 'openai', | |
| params: '120B', | |
| quantization: 'Q4_K_M', | |
| raw: 'openai/gpt-oss-120b-Q4_K_M', | |
| tags: [] | |
| }); | |
| expect(parseModelId('openai/gpt-oss-20b-F16')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'gpt-oss', | |
| orgName: 'openai', | |
| params: '20B', | |
| quantization: 'F16', | |
| raw: 'openai/gpt-oss-20b-F16', | |
| tags: [] | |
| }); | |
| expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toStrictEqual({ | |
| activatedParams: null, | |
| modelName: 'nomic-embed-text-v2-moe', | |
| orgName: null, | |
| params: null, | |
| quantization: 'Q4_K_M', | |
| raw: 'nomic-embed-text-v2-moe.Q4_K_M', | |
| tags: [] | |
| }); | |
| }); | |
| it('handles ambiguous model names', () => { | |
| // Qwen3.5 Instruct vs Thinking — tags should distinguish them | |
| expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Instruct')).toMatchObject({ | |
| modelName: 'Qwen3.5', | |
| params: '30B', | |
| activatedParams: 'A3B', | |
| tags: ['Instruct'] | |
| }); | |
| expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Thinking')).toMatchObject({ | |
| modelName: 'Qwen3.5', | |
| params: '30B', | |
| activatedParams: 'A3B', | |
| tags: ['Thinking'] | |
| }); | |
| // Dot-separated quantization with variant suffixes | |
| expect(parseModelId('gemma-3-27b-it-heretic-v2.Q8_0')).toMatchObject({ | |
| modelName: 'gemma-3', | |
| params: '27B', | |
| quantization: 'Q8_0', | |
| tags: ['it', 'heretic', 'v2'] | |
| }); | |
| expect(parseModelId('gemma-3-27b-it.Q8_0')).toMatchObject({ | |
| modelName: 'gemma-3', | |
| params: '27B', | |
| quantization: 'Q8_0', | |
| tags: ['it'] | |
| }); | |
| }); | |
| }); | |