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_whisper_enc::build() { | |
| const int n_frames = img.nx(); | |
| const int n_pos = n_frames / 2; | |
| GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos); | |
| ggml_tensor * inp = build_inp_raw(1); | |
| // conv1d block | |
| { | |
| // convolution + gelu | |
| ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); | |
| cur = ggml_add(ctx0, cur, model.conv1d_1_b); | |
| cur = ggml_gelu_erf(ctx0, cur); | |
| cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); | |
| cur = ggml_add(ctx0, cur, model.conv1d_2_b); | |
| cur = ggml_gelu_erf(ctx0, cur); | |
| // transpose | |
| inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); | |
| cb(inp, "after_conv1d", -1); | |
| } | |
| // sanity check (only check one layer, but it should be the same for all) | |
| GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b); | |
| GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b); | |
| GGML_ASSERT(model.layers[0].q_b); | |
| GGML_ASSERT(model.layers[0].v_b); | |
| GGML_ASSERT(!model.layers[0].k_b); // no bias for k | |
| ggml_tensor * pos_embd_selected = ggml_view_2d( | |
| ctx0, model.position_embeddings, | |
| model.position_embeddings->ne[0], n_pos, | |
| model.position_embeddings->nb[1], 0 | |
| ); | |
| ggml_tensor * cur = build_vit( | |
| inp, n_pos, | |
| NORM_TYPE_NORMAL, | |
| hparams.ffn_op, | |
| pos_embd_selected, | |
| nullptr); | |
| cb(cur, "after_transformer", -1); | |
| if (model.audio_has_stack_frames()) { | |
| // StackAudioFrames | |
| // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py | |
| cur = build_stack(cur, hparams.proj_stack_factor, n_embd); | |
| cb(cur, "after_stacked", -1); | |
| } | |
| if (proj_type == PROJECTOR_TYPE_ULTRAVOX) { | |
| // UltravoxProjector | |
| // pre-norm | |
| cur = ggml_rms_norm(ctx0, cur, 1e-6); | |
| cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); | |
| // ffn in | |
| cur = build_mm(model.mm_1_w, cur); | |
| // swiglu | |
| // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half | |
| cur = ggml_swiglu_swapped(ctx0, cur); | |
| // mid-norm | |
| cur = ggml_rms_norm(ctx0, cur, 1e-6); | |
| cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w); | |
| // ffn out | |
| cur = build_mm(model.mm_2_w, cur); | |
| } else if (proj_type == PROJECTOR_TYPE_QWEN2A) { | |
| // projector | |
| cur = build_mm(model.mm_fc_w, cur); | |
| cur = ggml_add(ctx0, cur, model.mm_fc_b); | |
| } else if (proj_type == PROJECTOR_TYPE_VOXTRAL) { | |
| // projector | |
| cur = build_ffn(cur, | |
| model.mm_1_w, model.mm_1_b, | |
| nullptr, nullptr, | |
| model.mm_2_w, model.mm_2_b, | |
| FFN_GELU_ERF, | |
| -1); | |
| } else if (proj_type == PROJECTOR_TYPE_MUSIC_FLAMINGO) { | |
| // projector | |
| cur = build_ffn(cur, | |
| model.mm_1_w, model.mm_1_b, | |
| nullptr, nullptr, | |
| model.mm_2_w, model.mm_2_b, | |
| FFN_GELU_ERF, | |
| -1); | |
| } else if (proj_type == PROJECTOR_TYPE_MERALION) { | |
| // stack (above) -> ln -> linear0+silu -> GLU -> out | |
| cur = ggml_norm(ctx0, cur, hparams.eps); | |
| cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); | |
| cur = ggml_add(ctx0, cur, model.mm_norm_pre_b); | |
| cur = ggml_mul_mat(ctx0, model.mm_0_w, cur); | |
| cur = ggml_add(ctx0, cur, model.mm_0_b); | |
| cur = ggml_silu(ctx0, cur); | |
| ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_1_w, cur); | |
| gate = ggml_add(ctx0, gate, model.mm_1_b); | |
| gate = ggml_silu(ctx0, gate); | |
| ggml_tensor * pool = ggml_mul_mat(ctx0, model.mm_2_w, cur); | |
| pool = ggml_add(ctx0, pool, model.mm_2_b); | |
| cur = ggml_mul(ctx0, gate, pool); | |
| cur = ggml_mul_mat(ctx0, model.mm_3_w, cur); | |
| cur = ggml_add(ctx0, cur, model.mm_3_b); | |
| } else if (proj_type == PROJECTOR_TYPE_GLMA) { | |
| cur = ggml_norm(ctx0, cur, hparams.eps); | |
| cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); | |
| cur = ggml_add(ctx0, cur, model.mm_norm_pre_b); | |
| cur = build_stack(cur, hparams.proj_stack_factor, n_embd); | |
| cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_2_w, model.mm_2_b, hparams.ffn_op, 0); | |
| cur = ggml_concat(ctx0, model.mm_boi, cur, 1); | |
| cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); | |
| } else { | |
| GGML_ABORT("%s: unknown projector type", __func__); | |
| } | |
| cb(cur, "projected", -1); | |
| ggml_build_forward_expand(gf, cur); | |
| return gf; | |
| } | |