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
| name: AI review (issues) | |
| on: | |
| issues: | |
| types: [opened] | |
| jobs: | |
| find-related: | |
| if: github.event.action == 'opened' | |
| runs-on: [self-hosted, opencode] | |
| permissions: | |
| contents: read | |
| issues: write | |
| steps: | |
| - name: Checkout repository | |
| uses: actions/checkout@v6 | |
| with: | |
| fetch-depth: 1 | |
| - name: Find related | |
| env: | |
| GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | |
| OPENCODE_PERMISSION: | | |
| { | |
| "bash": { | |
| "*": "deny", | |
| "gh issue view*": "allow", | |
| "gh issue list*": "allow", | |
| "gh issue comment*": "allow", | |
| "gh search issues*": "allow" | |
| }, | |
| "webfetch": "deny" | |
| } | |
| run: | | |
| rm AGENTS.md | |
| rm CLAUDE.md | |
| timeout 5m opencode run -m llama.cpp-dgx/ai-review-issues-find-similar --thinking "A new issue has been created: | |
| Issue number: ${{ github.event.issue.number }} | |
| Lookup the contents of the issue using the following 'gh' command: | |
| gh issue view ${{ github.event.issue.number }} --json title,body,url,number | |
| Next, perform the following task and then post a SINGLE comment (if needed). | |
| --- | |
| TASK : FIND RELATED ISSUES | |
| Using the 'gh' CLI tool, search through existing issues on Github. | |
| Find related or similar issues to the newly created one and list them. | |
| Do not list the new issue itself (it is #${{ github.event.issue.number }}). | |
| Consider: | |
| 1. Similar titles or descriptions | |
| 2. Same error messages or symptoms | |
| 3. Related functionality or components | |
| 4. Similar feature requests | |
| --- | |
| POSTING YOUR COMMENT: | |
| Based on your findings, post a SINGLE comment on issue #${{ github.event.issue.number }}. Build the comment as follows: | |
| - If no related issues were found, do NOT comment at all. | |
| - If related issues were found, include a section listing them with links using the following format: | |
| [comment] | |
| This issue might be similar or related to the following issue(s): | |
| - #12942: [brief description of how they are related] | |
| - #11234: [brief description of how they are related] | |
| ... | |
| _This comment was auto-generated locally using **$GA_ENGINE** on **$GA_MACHINE**_ | |
| [/comment] | |
| Remember: | |
| - Do not include the comment tags in your actual comment. | |
| - Post at most ONE comment combining all findings. | |
| - If you didn't find issues that are related enough, post nothing. | |
| - You have access only to the 'gh' CLI tool - don't try to use other tools. | |
| - If the output from a tool call is too long, try to limit down the search. | |
| " | |