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 bool common_imatrix_load_legacy(const std::string & fname, common_imatrix & imatrix) { | |
| std::ifstream in(fname, std::ios::binary); | |
| if (!in) { | |
| LOG_ERR("%s: failed to open %s\n", __func__, fname.c_str()); | |
| return false; | |
| } | |
| int n_entries; | |
| in.read((char *) &n_entries, sizeof(n_entries)); | |
| if (in.fail() || n_entries < 1) { | |
| LOG_ERR("%s: no data in file %s\n", __func__, fname.c_str()); | |
| return false; | |
| } | |
| for (int i = 0; i < n_entries; ++i) { | |
| int32_t len = 0; | |
| in.read((char *) &len, sizeof(len)); | |
| std::vector<char> name_as_vec(len + 1); | |
| in.read((char *) name_as_vec.data(), len); | |
| if (in.fail()) { | |
| LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname.c_str()); | |
| return false; | |
| } | |
| name_as_vec[len] = 0; | |
| std::string name{ name_as_vec.data() }; | |
| int32_t ncall = 0; | |
| in.read((char *) &ncall, sizeof(ncall)); | |
| int32_t nval = 0; | |
| in.read((char *) &nval, sizeof(nval)); | |
| if (in.fail() || nval < 1) { | |
| LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i); | |
| return false; | |
| } | |
| auto & e = imatrix.entries[std::move(name)]; | |
| e.sums.resize(nval); | |
| in.read((char *) e.sums.data(), nval * sizeof(float)); | |
| if (in.fail()) { | |
| LOG_ERR("%s: failed reading data for entry %d\n", __func__, i); | |
| return false; | |
| } | |
| e.counts.resize(1); | |
| e.counts[0] = ncall; | |
| } | |
| // the trailing data (chunk count + dataset name) is optional | |
| if (in.peek() != EOF) { | |
| int32_t n_calls = 0; | |
| in.read((char *) &n_calls, sizeof(n_calls)); | |
| imatrix.chunk_count = n_calls; | |
| if (!in.fail()) { | |
| int32_t len = 0; | |
| in.read((char *) &len, sizeof(len)); | |
| if (!in.fail() && len > 0) { | |
| std::vector<char> dataset(len + 1, 0); | |
| in.read(dataset.data(), len); | |
| if (!in.fail()) { | |
| imatrix.datasets.push_back(dataset.data()); | |
| } | |
| } | |
| } | |
| } | |
| imatrix.chunk_size = 0; | |
| imatrix.is_legacy = true; | |
| return true; | |
| } | |
| bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) { | |
| struct ggml_context * ctx = nullptr; | |
| struct gguf_init_params meta_gguf_params = { | |
| /* .no_alloc = */ false, | |
| /* .ctx = */ &ctx, | |
| }; | |
| struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), meta_gguf_params); | |
| if (!ctx_gguf) { | |
| return common_imatrix_load_legacy(fname, imatrix); | |
| } | |
| const int32_t n_entries = gguf_get_n_tensors(ctx_gguf); | |
| if (n_entries < 1) { | |
| LOG_ERR("%s: no data in file %s\n", __func__, fname.c_str()); | |
| gguf_free(ctx_gguf); | |
| ggml_free(ctx); | |
| return false; | |
| } | |
| const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS); | |
| const int64_t chunk_count_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT); | |
| const int64_t chunk_size_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE); | |
| if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) { | |
| const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key); | |
| imatrix.datasets.reserve(imatrix.datasets.size() + n); | |
| for (int64_t i = 0; i < n; ++i) { | |
| imatrix.datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i)); | |
| } | |
| } | |
| imatrix.has_metadata = (datasets_key != -1 && chunk_count_key != -1 && chunk_size_key != -1); | |
| imatrix.chunk_count = (chunk_count_key != -1) ? gguf_get_val_u32(ctx_gguf, chunk_count_key) : 0; | |
| imatrix.chunk_size = (chunk_size_key != -1) ? gguf_get_val_u32(ctx_gguf, chunk_size_key) : 0; | |
| const std::string in_sum2_suffix{ ".in_sum2" }; | |
| const std::string counts_suffix{ ".counts" }; | |
| std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for; | |
| for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { | |
| std::string name = cur->name; | |
| if (name.empty()) { continue; } | |
| if (string_remove_suffix(name, in_sum2_suffix)) { | |
| sums_counts_for[std::move(name)].first = cur; | |
| } else if (string_remove_suffix(name, counts_suffix)) { | |
| sums_counts_for[std::move(name)].second = cur; | |
| } | |
| } | |
| for (const auto & sc : sums_counts_for) { | |
| const std::string & name = sc.first; | |
| const struct ggml_tensor * in_sum2 = sc.second.first; | |
| const struct ggml_tensor * counts = sc.second.second; | |
| if (!in_sum2 || !counts) { | |
| LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str()); | |
| gguf_free(ctx_gguf); | |
| ggml_free(ctx); | |
| return false; | |
| } | |
| auto & e = imatrix.entries[name]; | |
| const int64_t nval = ggml_nelements(in_sum2); | |
| const int64_t ncounts = ggml_nelements(counts); | |
| e.sums.resize(nval); | |
| for (int64_t j = 0; j < nval; ++j) { | |
| e.sums[j] = ((const float *) in_sum2->data)[j]; | |
| } | |
| e.counts.resize(ncounts); | |
| for (int64_t j = 0; j < ncounts; ++j) { | |
| e.counts[j] = std::lround(((const float *) counts->data)[j]); | |
| } | |
| } | |
| gguf_free(ctx_gguf); | |
| ggml_free(ctx); | |
| return true; | |
| } | |