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
| struct common_params_model; | |
| using common_header = std::pair<std::string, std::string>; | |
| using common_header_list = std::vector<common_header>; | |
| struct common_download_progress { | |
| std::string url; | |
| size_t downloaded = 0; | |
| size_t total = 0; | |
| bool cached = false; | |
| }; | |
| class common_download_callback { | |
| public: | |
| virtual ~common_download_callback() = default; | |
| virtual void on_start(const common_download_progress & p) = 0; | |
| virtual void on_update(const common_download_progress & p) = 0; | |
| virtual void on_done(const common_download_progress & p, bool ok) = 0; | |
| virtual bool is_cancelled() const { return false; } | |
| }; | |
| struct common_remote_params { | |
| common_header_list headers; | |
| long timeout = 0; // in seconds, 0 means no timeout | |
| long max_size = 0; // unlimited if 0 | |
| }; | |
| // get remote file content, returns <http_code, raw_response_body> | |
| std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params); | |
| // split HF repo with tag into <repo, tag>, for example: | |
| // - "ggml-org/models:F16" -> <"ggml-org/models", "F16"> | |
| // tag is optional and can be empty | |
| std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag); | |
| // Result of common_list_cached_models | |
| struct common_cached_model_info { | |
| std::string repo; | |
| std::string tag; | |
| std::string to_string() const { | |
| return repo + ":" + tag; | |
| } | |
| }; | |
| // Options for common_download_file_single | |
| struct common_download_opts { | |
| std::string bearer_token; | |
| common_header_list headers; | |
| bool offline = false; | |
| bool download_mmproj = false; | |
| bool download_mtp = false; | |
| common_download_callback * callback = nullptr; | |
| }; | |
| struct common_download_task { | |
| common_download_opts opts; | |
| std::string url; | |
| std::string local_path; | |
| std::function<void()> on_done; | |
| bool is_hf = false; | |
| common_download_task() = default; | |
| common_download_task(hf_cache::hf_file f, | |
| const common_download_opts & opts, | |
| std::function<void()> on_done = nullptr) | |
| : opts(opts), url(f.url), local_path(f.local_path), on_done(on_done), is_hf(true) {} | |
| }; | |
| void common_download_run_tasks(const std::vector<common_download_task> & tasks); | |
| // if url is a multi-part GGUF file, returns all parts, otherwise returns the single file | |
| std::vector<std::string> common_download_get_all_parts(const std::string & url); | |
| // returns list of cached models | |
| std::vector<common_cached_model_info> common_list_cached_models(); | |
| // download single file from url to local path | |
| // returns status code or -1 on error | |
| // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash) | |
| int common_download_file_single(const std::string & url, | |
| const std::string & path, | |
| const common_download_opts & opts = {}, | |
| bool skip_etag = false); | |
| // resolve and download model from Docker registry | |
| // return local path to downloaded model file | |
| std::string common_docker_resolve_model(const std::string & docker); | |
| // Remove a cached model from disk | |
| // input format: "user/model" or "user/model:tag" | |
| // - if tag is omitted, removes the entire repo cache directory | |
| // - if tag is present, removes only files matching that tag (and orphaned blobs) | |
| // returns true if anything was removed | |
| bool common_download_remove(const std::string & hf_repo_with_tag); | |
| struct common_download_hf_plan { | |
| hf_cache::hf_file primary; | |
| hf_cache::hf_files model_files; | |
| hf_cache::hf_file mmproj; | |
| hf_cache::hf_file mtp; | |
| hf_cache::hf_file preset; // if set, only this file is downloaded | |
| }; | |
| common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts); | |