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
| // | |
| // llama_kv_cache_dsa | |
| // | |
| llama_kv_cache_dsa::llama_kv_cache_dsa( | |
| const llama_model & model, | |
| ggml_type type_k, | |
| ggml_type type_v, | |
| bool v_trans, | |
| bool offload, | |
| bool unified, | |
| uint32_t kv_size, | |
| uint32_t n_seq_max, | |
| uint32_t n_pad, | |
| uint32_t n_swa, | |
| llama_swa_type swa_type, | |
| const layer_filter_cb & filter, | |
| const layer_reuse_cb & reuse) : | |
| hparams_lid(model.hparams), n_stream(unified ? 1 : n_seq_max) { | |
| LLAMA_LOG_INFO("%s: creating main KV cache, size = %u cells\n", __func__, kv_size); | |
| kv_mla = std::make_unique<llama_kv_cache>( | |
| model, model.hparams, type_k, type_v, | |
| v_trans, offload, unified, kv_size, n_seq_max, n_pad, | |
| n_swa, swa_type, nullptr, filter, reuse, nullptr); | |
| // we use llama_kv_cache for caching indexer keys | |
| // by hand-tweaking some hparams we fool it to create | |
| // indexer key cache tensors with correct dimensions | |
| // https://github.com/ggml-org/llama.cpp/pull/21149#discussion_r3015940823 | |
| // DSA lightning indexer uses MQA with single key head | |
| std::fill(hparams_lid.n_head_kv_arr.begin(), hparams_lid.n_head_kv_arr.end(), 1); | |
| hparams_lid.n_embd_head_k_full = model.hparams.indexer_head_size; | |
| hparams_lid.rope_type = LLAMA_ROPE_TYPE_NEOX; | |
| LLAMA_LOG_INFO("%s: creating indexer KV cache, size = %u cells\n", __func__, kv_size); | |
| kv_lid = std::make_unique<llama_kv_cache>( | |
| model, hparams_lid, type_k, type_v, | |
| v_trans, offload, unified, kv_size, n_seq_max, n_pad, | |
| n_swa, swa_type, nullptr, filter, reuse, nullptr); | |
| } | |
| void llama_kv_cache_dsa::clear(bool data) { | |
| kv_mla->clear(data); | |
| kv_lid->clear(data); | |
| } | |
| bool llama_kv_cache_dsa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { | |
| bool res = true; | |
| res = res & kv_mla->seq_rm(seq_id, p0, p1); | |
| res = res & kv_lid->seq_rm(seq_id, p0, p1); | |
| return res; | |
| } | |
| void llama_kv_cache_dsa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { | |
| kv_mla->seq_cp(seq_id_src, seq_id_dst, p0, p1); | |
| kv_lid->seq_cp(seq_id_src, seq_id_dst, p0, p1); | |
| } | |
| void llama_kv_cache_dsa::seq_keep(llama_seq_id seq_id) { | |
| kv_mla->seq_keep(seq_id); | |
| kv_lid->seq_keep(seq_id); | |
| } | |
| void llama_kv_cache_dsa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) { | |
| kv_mla->seq_add(seq_id, p0, p1, shift); | |
| kv_lid->seq_add(seq_id, p0, p1, shift); | |
| } | |
| void llama_kv_cache_dsa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { | |
| kv_mla->seq_div(seq_id, p0, p1, d); | |
| kv_lid->seq_div(seq_id, p0, p1, d); | |
| } | |
| llama_pos llama_kv_cache_dsa::seq_pos_min(llama_seq_id seq_id) const { | |
| return kv_mla->seq_pos_min(seq_id); | |
| } | |
| llama_pos llama_kv_cache_dsa::seq_pos_max(llama_seq_id seq_id) const { | |
| return kv_mla->seq_pos_max(seq_id); | |
| } | |
| std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsa::memory_breakdown() const { | |
| std::map<ggml_backend_buffer_type_t, size_t> mb = kv_mla->memory_breakdown(); | |
| for (const auto & buft_size : kv_lid->memory_breakdown()) { | |
| mb[buft_size.first] += buft_size.second; | |
| } | |
| return mb; | |
| } | |
| llama_memory_context_ptr llama_kv_cache_dsa::init_batch( | |
| llama_batch_allocr & balloc, | |
| uint32_t n_ubatch, | |
| bool embd_all) { | |
| GGML_UNUSED(embd_all); | |
| do { | |
| balloc.split_reset(); | |
| std::vector<llama_ubatch> ubatches; | |
| while (true) { | |
| auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true); | |
| if (ubatch.n_tokens == 0) { | |
| break; | |
| } | |
| ubatches.push_back(std::move(ubatch)); // NOLINT | |
| } | |
| if (balloc.get_n_used() < balloc.get_n_tokens()) { | |
| // failed to find a suitable split | |
| break; | |
| } | |
| auto sinfos_mla = kv_mla->prepare(ubatches); | |
| if (sinfos_mla.empty()) { | |
| break; | |
| } | |
| auto sinfos_lid = kv_lid->prepare(ubatches); | |
| if (sinfos_lid.empty()) { | |
| break; | |
| } | |
| assert(sinfos_mla.size() == sinfos_lid.size()); | |
| return std::make_unique<llama_kv_cache_dsa_context>( | |
| this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(ubatches)); | |
| } while (false); | |
| return std::make_unique<llama_kv_cache_dsa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE); | |
| } | |
| llama_memory_context_ptr llama_kv_cache_dsa::init_full() { | |
| return std::make_unique<llama_kv_cache_dsa_context>(this); | |
| } | |
| llama_memory_context_ptr llama_kv_cache_dsa::init_update(llama_context * lctx, bool optimize) { | |
| return std::make_unique<llama_kv_cache_dsa_context>(this, lctx, optimize); | |
| } | |
| bool llama_kv_cache_dsa::get_can_shift() const { | |
| return kv_mla->get_can_shift() && | |
| kv_lid->get_can_shift() && | |
| kv_mla->get_size() == kv_lid->get_size(); | |
| } | |
| void llama_kv_cache_dsa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const { | |
| kv_mla->state_write(io, seq_id, flags); | |
| kv_lid->state_write(io, seq_id, flags); | |
| } | |
| void llama_kv_cache_dsa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) { | |
| kv_mla->state_read(io, seq_id, flags); | |
| kv_lid->state_read(io, seq_id, flags); | |
| } | |
| llama_kv_cache * llama_kv_cache_dsa::get_mla() const { | |
| return kv_mla.get(); | |
| } | |
| llama_kv_cache * llama_kv_cache_dsa::get_lid() const { | |
| return kv_lid.get(); | |
| } | |
| // | |
| // llama_kv_cache_dsa_context | |
| // | |
| llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(llama_memory_status status) : status(status) {} | |
| llama_kv_cache_dsa_context::llama_kv_cache_dsa_context( | |
| llama_kv_cache_dsa * kv) : | |
| ctx_mla(kv->get_mla()->init_full()), | |
| ctx_lid(kv->get_lid()->init_full()), | |
| status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) { | |
| } | |
| llama_kv_cache_dsa_context::llama_kv_cache_dsa_context( | |
| llama_kv_cache_dsa * kv, | |
| llama_context * lctx, | |
| bool optimize) : | |
| ctx_mla(kv->get_mla()->init_update(lctx, optimize)), | |
| ctx_lid(kv->get_lid()->init_update(lctx, optimize)), | |
| status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) { | |
| } | |
| llama_kv_cache_dsa_context::llama_kv_cache_dsa_context( | |
| llama_kv_cache_dsa * kv, | |
| slot_info_vec_t sinfos_mla, | |
| slot_info_vec_t sinfos_lid, | |
| std::vector<llama_ubatch> ubatches) : | |
| ubatches(std::move(ubatches)), | |
| // note: here we copy the ubatches. not sure if this is ideal | |
| ctx_mla(new llama_kv_cache_context(kv->get_mla(), std::move(sinfos_mla), this->ubatches)), | |
| ctx_lid(new llama_kv_cache_context(kv->get_lid(), std::move(sinfos_lid), this->ubatches)), | |
| status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) { | |
| } | |
| llama_kv_cache_dsa_context:: ~llama_kv_cache_dsa_context() = default; | |
| bool llama_kv_cache_dsa_context::next() { | |
| assert(status == LLAMA_MEMORY_STATUS_SUCCESS); | |
| ctx_mla->next(); | |
| ctx_lid->next(); | |
| if (++i_next >= ubatches.size()) { | |
| return false; | |
| } | |
| return true; | |
| } | |
| bool llama_kv_cache_dsa_context::apply() { | |
| assert(!llama_memory_status_is_fail(status)); | |
| bool res = true; | |
| res = res & ctx_mla->apply(); | |
| res = res & ctx_lid->apply(); | |
| return res; | |
| } | |
| llama_memory_status llama_kv_cache_dsa_context::get_status() const { | |
| return status; | |
| } | |
| const llama_ubatch & llama_kv_cache_dsa_context::get_ubatch() const { | |
| assert(status == LLAMA_MEMORY_STATUS_SUCCESS); | |
| return ubatches[i_next]; | |
| } | |
| const llama_kv_cache_context * llama_kv_cache_dsa_context::get_mla() const { | |
| assert(status == LLAMA_MEMORY_STATUS_SUCCESS); | |
| return static_cast<const llama_kv_cache_context *>(ctx_mla.get()); | |
| } | |
| const llama_kv_cache_context * llama_kv_cache_dsa_context::get_lid() const { | |
| assert(status == LLAMA_MEMORY_STATUS_SUCCESS); | |
| return static_cast<const llama_kv_cache_context *>(ctx_lid.get()); | |
| } | |