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
| enum common_reasoning_budget_state { | |
| REASONING_BUDGET_IDLE, // waiting for start sequence | |
| REASONING_BUDGET_COUNTING, // counting down tokens | |
| REASONING_BUDGET_FORCING, // forcing budget message + end sequence | |
| REASONING_BUDGET_WAITING_UTF8, // budget exhausted, waiting for UTF-8 completion | |
| REASONING_BUDGET_DONE, // passthrough forever | |
| }; | |
| // Creates a reasoning budget sampler that limits token generation inside a | |
| // reasoning block (e.g. between <think> and </think>). | |
| // | |
| // State machine: IDLE -> COUNTING -> WAITING_UTF8 -> FORCING -> DONE | |
| // IDLE: passthrough, watching for start_tokens sequence | |
| // COUNTING: counting down remaining tokens, watching for natural end_tokens | |
| // WAITING_UTF8: budget exhausted, allowing tokens to complete a UTF-8 sequence | |
| // FORCING: forces forced_tokens token-by-token (all other logits -> -inf) | |
| // DONE: passthrough forever | |
| // | |
| // Parameters: | |
| // vocab - vocabulary (used for UTF-8 boundary detection; can be nullptr) | |
| // start_tokens - token sequence that activates counting | |
| // end_tokens - token sequence for natural deactivation | |
| // forced_tokens - token sequence forced when budget expires | |
| // budget - max tokens allowed in the reasoning block | |
| // initial_state - initial state | |
| // | |
| struct llama_sampler * common_reasoning_budget_init( | |
| const struct llama_vocab * vocab, | |
| const std::vector<llama_token> & start_tokens, | |
| const std::vector<llama_token> & end_tokens, | |
| const std::vector<llama_token> & forced_tokens, | |
| int32_t budget, | |
| common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE); | |
| common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl); | |
| // Manually transition the reasoning budget sampler into the FORCING state. | |
| // Returns true if the transition occurred. | |
| bool common_reasoning_budget_force(struct llama_sampler * smpl); | |