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
| ggml_cgraph * clip_graph_kimivl::build() { | |
| // 2D input positions | |
| ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); | |
| ggml_set_name(pos_h, "pos_h"); | |
| ggml_set_input(pos_h); | |
| ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); | |
| ggml_set_name(pos_w, "pos_w"); | |
| ggml_set_input(pos_w); | |
| ggml_tensor * learned_pos_embd = resize_position_embeddings(); | |
| // build ViT with 2D position embeddings | |
| auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { | |
| // first half is X axis and second half is Y axis | |
| return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); | |
| }; | |
| ggml_tensor * inp = build_inp(); | |
| ggml_tensor * cur = build_vit( | |
| inp, n_patches, | |
| NORM_TYPE_NORMAL, | |
| hparams.ffn_op, | |
| learned_pos_embd, | |
| add_pos); | |
| cb(cur, "vit_out", -1); | |
| { | |
| // patch_merger | |
| const int scale_factor = model.hparams.n_merge; | |
| cur = build_patch_merge_permute(cur, scale_factor); | |
| // projection norm | |
| int proj_inp_dim = cur->ne[0]; | |
| cur = ggml_view_2d(ctx0, cur, | |
| n_embd, cur->ne[1] * scale_factor * scale_factor, | |
| ggml_row_size(cur->type, n_embd), 0); | |
| cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm | |
| cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); | |
| cur = ggml_add(ctx0, cur, model.mm_input_norm_b); | |
| cur = ggml_view_2d(ctx0, cur, | |
| proj_inp_dim, cur->ne[1] / scale_factor / scale_factor, | |
| ggml_row_size(cur->type, proj_inp_dim), 0); | |
| cb(cur, "proj_inp_normed", -1); | |
| // projection mlp | |
| cur = build_ffn(cur, | |
| model.mm_1_w, model.mm_1_b, | |
| nullptr, nullptr, | |
| model.mm_2_w, model.mm_2_b, | |
| FFN_GELU, | |
| -1); | |
| cb(cur, "proj_out", -1); | |
| } | |
| // build the graph | |
| ggml_build_forward_expand(gf, cur); | |
| return gf; | |
| } | |