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
| static llama_context * make_ctx(const common_params & params, llama_model * model) { | |
| auto cparams = common_context_params_to_llama(params); | |
| cparams.n_seq_max = 1; | |
| cparams.n_rs_seq = 8; | |
| cparams.n_batch = std::max(cparams.n_batch, (uint32_t) (cparams.n_rs_seq + 1)); | |
| cparams.n_ubatch = std::max(cparams.n_ubatch, (uint32_t) (cparams.n_rs_seq + 1)); | |
| return llama_init_from_model(model, cparams); | |
| } | |
| static bool decode_tokens(llama_context * ctx, const std::vector<llama_token> & tokens, uint32_t count) { | |
| llama_batch batch = llama_batch_init(count, 0, 1); | |
| for (uint32_t pos = 0; pos < count; ++pos) { | |
| common_batch_add(batch, tokens[pos], pos, { 0 }, false); | |
| } | |
| const bool ok = llama_decode(ctx, batch) == 0; | |
| llama_batch_free(batch); | |
| return ok; | |
| } | |
| static bool decode_one(llama_context * ctx, llama_token tok, llama_pos pos) { | |
| llama_batch batch = llama_batch_init(1, 0, 1); | |
| common_batch_add(batch, tok, pos, { 0 }, true); | |
| const bool ok = llama_decode(ctx, batch) == 0; | |
| llama_batch_free(batch); | |
| return ok; | |
| } | |
| int main(int argc, char ** argv) { | |
| std::setlocale(LC_NUMERIC, "C"); | |
| common_params params; | |
| params.sampling.seed = 1234; | |
| params.n_predict = 1; | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { | |
| return 1; | |
| } | |
| ggml_backend_load_all(); | |
| common_init_result_ptr llama_init = common_init_from_params(params); | |
| llama_model * model = llama_init->model(); | |
| if (model == nullptr) { | |
| fprintf(stderr, "%s : failed to init model\n", __func__); | |
| return 1; | |
| } | |
| if (!llama_model_is_recurrent(model) && !llama_model_is_hybrid(model)) { | |
| fprintf(stderr, "%s : skipping for non-recurrent model\n", __func__); | |
| return 0; | |
| } | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| const int n_vocab = llama_vocab_n_tokens(vocab); | |
| llama_context * ctx_src = make_ctx(params, model); | |
| llama_context * ctx_dst = make_ctx(params, model); | |
| if (ctx_src == nullptr || ctx_dst == nullptr) { | |
| fprintf(stderr, "%s : failed to init contexts\n", __func__); | |
| return 1; | |
| } | |
| if (llama_n_rs_seq(ctx_src) == 0) { | |
| fprintf(stderr, "%s : skipping because n_rs_seq is disabled\n", __func__); | |
| llama_free(ctx_src); | |
| llama_free(ctx_dst); | |
| return 0; | |
| } | |
| std::vector<llama_token> tokens = common_tokenize(ctx_src, "The quick brown fox jumps", true); | |
| const uint32_t n_rs_seq = llama_n_rs_seq(ctx_src); | |
| if (tokens.size() > n_rs_seq + 1) { | |
| tokens.resize(n_rs_seq + 1); | |
| } | |
| if (tokens.size() < 2) { | |
| fprintf(stderr, "%s : not enough prompt tokens\n", __func__); | |
| return 1; | |
| } | |
| const uint32_t n_tokens = tokens.size(); | |
| const llama_token last_tok = tokens.back(); | |
| const llama_pos last_pos = (llama_pos) n_tokens - 2; | |
| // Decode the full prompt on the source, then roll back the last position. | |
| // Rollback leaves the recurrent memory in a snapshot state (rs_idx != 0). | |
| if (!decode_tokens(ctx_src, tokens, n_tokens)) { | |
| fprintf(stderr, "%s : failed to decode prompt\n", __func__); | |
| return 1; | |
| } | |
| if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, last_pos, -1)) { | |
| fprintf(stderr, "%s : rollback failed\n", __func__); | |
| return 1; | |
| } | |
| // Save the rolled-back state and restore it into a fresh context. | |
| common_prompt_checkpoint ckpt; | |
| ckpt.update_tgt(ctx_src, 0, 0); | |
| ckpt.load_tgt(ctx_dst, 0, 0); | |
| // Replay the rolled-back token on both contexts and compare logits. | |
| if (!decode_one(ctx_src, last_tok, last_pos) || | |
| !decode_one(ctx_dst, last_tok, last_pos)) { | |
| fprintf(stderr, "%s : replay failed\n", __func__); | |
| return 1; | |
| } | |
| const float * logits_src = llama_get_logits_ith(ctx_src, 0); | |
| const float * logits_dst = llama_get_logits_ith(ctx_dst, 0); | |
| if (logits_src == nullptr || logits_dst == nullptr) { | |
| fprintf(stderr, "%s : missing logits\n", __func__); | |
| return 1; | |
| } | |
| constexpr float eps = 1e-5f; | |
| for (int i = 0; i < n_vocab; ++i) { | |
| if (std::fabs(logits_src[i] - logits_dst[i]) > eps) { | |
| fprintf(stderr, "%s : logits mismatch at token %d (%g != %g)\n", | |
| __func__, i, (double) logits_src[i], (double) logits_dst[i]); | |
| return 1; | |
| } | |
| } | |
| // Repeat the load into a context that already has its own rollback state: | |
| // groups 1..n_rs_seq hold a *different* prompt's history, and rs_idx[0] is | |
| // non-zero at load time. The restore must wipe that state and still match. | |
| llama_context * ctx_dirty = make_ctx(params, model); | |
| if (ctx_dirty == nullptr) { | |
| fprintf(stderr, "%s : failed to init dirty ctx\n", __func__); | |
| return 1; | |
| } | |
| std::vector<llama_token> noise = tokens; | |
| for (auto & t : noise) { | |
| t = (t + 1) % n_vocab; | |
| if (t < 0) { | |
| t = 0; | |
| } | |
| } | |
| if (!decode_tokens(ctx_dirty, noise, n_tokens)) { | |
| fprintf(stderr, "%s : dirty prompt decode failed\n", __func__); | |
| return 1; | |
| } | |
| if (!llama_memory_seq_rm(llama_get_memory(ctx_dirty), 0, last_pos, -1)) { | |
| fprintf(stderr, "%s : dirty rollback failed\n", __func__); | |
| return 1; | |
| } | |
| ckpt.load_tgt(ctx_dirty, 0, 0); | |
| if (!decode_one(ctx_dirty, last_tok, last_pos)) { | |
| fprintf(stderr, "%s : dirty replay failed\n", __func__); | |
| return 1; | |
| } | |
| const float * logits_dirty = llama_get_logits_ith(ctx_dirty, 0); | |
| if (logits_dirty == nullptr) { | |
| fprintf(stderr, "%s : missing dirty logits\n", __func__); | |
| return 1; | |
| } | |
| for (int i = 0; i < n_vocab; ++i) { | |
| if (std::fabs(logits_src[i] - logits_dirty[i]) > eps) { | |
| fprintf(stderr, "%s : dirty-ctx logits mismatch at token %d (%g != %g)\n", | |
| __func__, i, (double) logits_src[i], (double) logits_dirty[i]); | |
| return 1; | |
| } | |
| } | |
| fprintf(stderr, "%s : recurrent rollback checkpoint restored successfully\n", __func__); | |
| llama_free(ctx_src); | |
| llama_free(ctx_dst); | |
| llama_free(ctx_dirty); | |
| return 0; | |
| } | |