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
| struct llama_sampler_llg { | |
| const llama_vocab * vocab; | |
| std::string grammar_kind; | |
| std::string grammar_data; | |
| LlgTokenizer * tokenizer; | |
| LlgMatcher * grammar; | |
| }; | |
| static LlgMatcher * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind, | |
| const char * grammar_data) { | |
| LlgConstraintInit cinit; | |
| llg_constraint_init_set_defaults(&cinit, tokenizer); | |
| const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL"); | |
| if (log_level && *log_level) { | |
| cinit.log_stderr_level = atoi(log_level); | |
| } | |
| auto c = llg_new_matcher(&cinit, grammar_kind, grammar_data); | |
| if (llg_matcher_get_error(c)) { | |
| LOG_ERR("llg error: %s\n", llg_matcher_get_error(c)); | |
| llg_free_matcher(c); | |
| return nullptr; | |
| } | |
| return c; | |
| } | |
| static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) { | |
| return "llguidance"; | |
| } | |
| static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) { | |
| auto * ctx = (llama_sampler_llg *) smpl->ctx; | |
| if (ctx->grammar) { | |
| llg_matcher_consume_token(ctx->grammar, token); | |
| } | |
| } | |
| static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) { | |
| auto * ctx = (llama_sampler_llg *) smpl->ctx; | |
| if (ctx->grammar) { | |
| const uint32_t * mask = llg_matcher_get_mask(ctx->grammar); | |
| if (mask == nullptr) { | |
| if (llg_matcher_compute_mask(ctx->grammar) == 0) { | |
| mask = llg_matcher_get_mask(ctx->grammar); | |
| } else { | |
| LOG_ERR("llg error: %s\n", llg_matcher_get_error(ctx->grammar)); | |
| llg_free_matcher(ctx->grammar); | |
| ctx->grammar = nullptr; | |
| return; | |
| } | |
| } | |
| for (size_t i = 0; i < cur_p->size; ++i) { | |
| auto token = cur_p->data[i].id; | |
| if ((mask[token / 32] & (1 << (token % 32))) == 0) { | |
| cur_p->data[i].logit = -INFINITY; | |
| } | |
| } | |
| } | |
| } | |
| static void llama_sampler_llg_reset(llama_sampler * smpl) { | |
| auto * ctx = (llama_sampler_llg *) smpl->ctx; | |
| if (ctx->grammar) { | |
| llg_matcher_reset(ctx->grammar); | |
| } | |
| } | |
| static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) { | |
| const auto * ctx = (const llama_sampler_llg *) smpl->ctx; | |
| auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr); | |
| // copy the state | |
| { | |
| auto * result_ctx = (llama_sampler_llg *) result->ctx; | |
| if (ctx->grammar) { | |
| result_ctx->grammar_kind = ctx->grammar_kind; | |
| result_ctx->grammar_data = ctx->grammar_data; | |
| result_ctx->grammar = llg_clone_matcher(ctx->grammar); | |
| result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer); | |
| } | |
| } | |
| return result; | |
| } | |
| static void llama_sampler_llg_free(llama_sampler * smpl) { | |
| const auto * ctx = (llama_sampler_llg *) smpl->ctx; | |
| if (ctx->grammar) { | |
| llg_free_matcher(ctx->grammar); | |
| llg_free_tokenizer(ctx->tokenizer); | |
| } | |
| delete ctx; | |
| } | |
| static llama_sampler_i llama_sampler_llg_i = { | |
| /* .name = */ llama_sampler_llg_name, | |
| /* .accept = */ llama_sampler_llg_accept_impl, | |
| /* .apply = */ llama_sampler_llg_apply, | |
| /* .reset = */ llama_sampler_llg_reset, | |
| /* .clone = */ llama_sampler_llg_clone, | |
| /* .free = */ llama_sampler_llg_free, | |
| /* .backend_init = */ NULL, | |
| /* .backend_accept = */ NULL, | |
| /* .backend_apply = */ NULL, | |
| /* .backend_set_input = */ NULL, | |
| }; | |
| static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len, | |
| uint32_t * output_tokens, size_t output_tokens_len) { | |
| const llama_vocab * vocab = (const llama_vocab *) user_data; | |
| int r = 0; | |
| try { | |
| r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false, | |
| true); | |
| } catch (const std::exception & e) { | |
| GGML_ABORT("llama_tokenize failed: %s\n", e.what()); | |
| } | |
| if (r < 0) { | |
| return -r; | |
| } | |
| return r; | |
| } | |
| static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) { | |
| // TODO store the tokenizer in the vocab somehow | |
| static const llama_vocab * vocab_cache; | |
| static LlgTokenizer * tokenizer_cache; | |
| if (vocab_cache == vocab) { | |
| return llg_clone_tokenizer(tokenizer_cache); | |
| } | |
| auto tok_eos = llama_vocab_eot(vocab); | |
| if (tok_eos == LLAMA_TOKEN_NULL) { | |
| tok_eos = llama_vocab_eos(vocab); | |
| } | |
| size_t vocab_size = llama_vocab_n_tokens(vocab); | |
| auto token_lens = new uint32_t[vocab_size]; | |
| // we typically have ~7 bytes per token; let's go on the safe side here | |
| auto token_bytes_size = vocab_size * 16 + 1024 * 1024; | |
| auto token_bytes = new uint8_t[token_bytes_size]; | |
| size_t offset = 0; | |
| for (size_t i = 0; i < vocab_size; i++) { | |
| size_t max_token = 1024; | |
| if (token_bytes_size - offset < max_token) { | |
| GGML_ABORT("token_bytes buffer too small\n"); | |
| } | |
| llama_token token = i; | |
| auto dp = (char *) token_bytes + offset; | |
| auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false); | |
| if (size < 0) { | |
| GGML_ABORT("llama_detokenize failed\n"); | |
| } | |
| if (size == 0) { | |
| size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true); | |
| if (size < 0) { | |
| GGML_ABORT("llama_detokenize failed\n"); | |
| } | |
| if (size != 0) { | |
| *dp = '\xff'; // special token prefix marker | |
| size += 1; | |
| } | |
| } | |
| token_lens[i] = size; | |
| offset += size; | |
| } | |
| LlgTokenizerInit tinit = { | |
| /* .vocab_size = */ (uint32_t) vocab_size, | |
| /* .tok_eos = */ (uint32_t) tok_eos, | |
| /* .token_lens = */ token_lens, | |
| /* .token_bytes = */ token_bytes, | |
| /* .tokenizer_json = */ nullptr, | |
| /* .tokenize_assumes_string = */ true, | |
| /* .tokenize_fn = */ llama_sampler_llg_tokenize_fn, | |
| /* .use_approximate_greedy_tokenize_fn = */ false, | |
| /* .tokenize_user_data = */ vocab, | |
| /* .slices = */ nullptr, | |
| }; | |
| char error_buffer[1024]; | |
| LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer)); | |
| delete[] token_bytes; | |
| delete[] token_lens; | |
| if (tokenizer == nullptr) { | |
| LOG_ERR("llg tokenizer error: %s\n", error_buffer); | |
| return tokenizer; | |
| } | |
| if (tokenizer_cache) { | |
| llg_free_tokenizer(tokenizer_cache); | |
| } | |
| vocab_cache = vocab; | |
| tokenizer_cache = tokenizer; | |
| return llg_clone_tokenizer(tokenizer_cache); | |
| } | |
| llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, | |
| const char * grammar_data) { | |
| auto * ctx = new llama_sampler_llg; | |
| if (grammar_kind != nullptr && grammar_kind[0] != '\0') { | |
| auto tokenizer = llama_sampler_llg_new_tokenizer(vocab); | |
| *ctx = { | |
| /* .vocab = */ vocab, | |
| /* .grammar_kind = */ grammar_kind, | |
| /* .grammar_data = */ grammar_data, | |
| /* .tokenizer = */ tokenizer, | |
| /* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data), | |
| }; | |
| if (ctx->grammar) { | |
| GGML_ASSERT(((size_t) llama_vocab_n_tokens(vocab) + 31) / 32 * 4 == | |
| llg_matcher_get_mask_byte_size(ctx->grammar)); | |
| } | |
| } else { | |
| *ctx = { | |
| /* .vocab = */ vocab, | |
| /* .grammar_kind = */ {}, | |
| /* .grammar_data = */ {}, | |
| /* .tokenizer = */ nullptr, | |
| /* .grammar = */ nullptr, | |
| }; | |
| } | |
| return llama_sampler_init( | |
| /* .iface = */ &llama_sampler_llg_i, | |
| /* .ctx = */ ctx); | |
| } | |
| llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) { | |
| LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); | |
| return nullptr; | |
| } | |