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
| void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, | |
| std::vector<llama_token> & inp, int nnew, bool print_progress) { | |
| const int64_t t_start_ms = ggml_time_ms(); | |
| const int64_t inp_size = inp.size(); | |
| const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1); | |
| int64_t n_done = 0; | |
| for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) { | |
| const int64_t i_start = std::max(inp_size - nnew, ngram_size); | |
| for (int64_t i = i_start; i < inp_size; ++i) { | |
| const int64_t ngram_start = i - ngram_size; | |
| common_ngram ngram(&inp[ngram_start], ngram_size); | |
| const llama_token token = inp[i]; | |
| common_ngram_cache::iterator part_it = ngram_cache.find(ngram); | |
| if (part_it == ngram_cache.end()) { | |
| common_ngram_cache_part part; | |
| part.emplace(token, 1); | |
| ngram_cache.emplace(ngram, part); | |
| } else { | |
| common_ngram_cache_part::iterator token_count_it = part_it->second.find(token); | |
| if (token_count_it == part_it->second.end()) { | |
| part_it->second.emplace(token, 1); | |
| } else { | |
| token_count_it->second++; | |
| } | |
| } | |
| ++n_done; | |
| if (print_progress && n_done % 10000000 == 0) { | |
| const int64_t t_now_ms = ggml_time_ms(); | |
| const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done; | |
| const int64_t eta_min = eta_ms / (60*1000); | |
| const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; | |
| fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s); | |
| } | |
| } | |
| } | |
| } | |
| // Helper function to get a token from the combined, speculative sequence of inp and draft. | |
| static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) { | |
| return i < inp.size() ? inp[i] : draft[1 + i - inp.size()]; | |
| } | |
| // If sample size or percentage are below these thresholds the draft is aborted early: | |
| constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1}; | |
| constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50}; | |
| constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2}; | |
| constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; | |
| // Helper function that tries to draft a token from only the static ngram cache: | |
| static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) { | |
| common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); | |
| if (part_static_it == nc_static.end()) { | |
| return LLAMA_TOKEN_NULL; | |
| } | |
| const common_ngram_cache_part part_static = part_static_it->second; | |
| int max_count_static = 0; | |
| int sum_count_static = 0; | |
| llama_token max_token = LLAMA_TOKEN_NULL; | |
| for (std::pair<llama_token, int> token_count_static : part_static) { | |
| const llama_token token = token_count_static.first; | |
| const int32_t count_static = token_count_static.second; | |
| if (count_static > max_count_static) { | |
| max_token = token; | |
| max_count_static = count_static; | |
| } | |
| sum_count_static += count_static; | |
| } | |
| if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { | |
| return LLAMA_TOKEN_NULL; | |
| } | |
| if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { | |
| return LLAMA_TOKEN_NULL; | |
| } | |
| return max_token; | |
| } | |
| // Try to draft a token from primary cache (context/dynamic), validate with static cache: | |
| static llama_token try_draft( | |
| common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static, | |
| const int * min_sample_size, const int * min_percent) { | |
| llama_token drafted_token = LLAMA_TOKEN_NULL; | |
| for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) { | |
| const common_ngram ngram_primary = ngrams_primary[i]; | |
| common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); | |
| if (part_primary_it == nc_primary.end()) { | |
| continue; | |
| } | |
| const common_ngram_cache_part part_primary = part_primary_it->second; | |
| int max_count_primary = 0; | |
| int max_count_static = 0; | |
| int sum_count_primary = 0; | |
| llama_token max_token = LLAMA_TOKEN_NULL; | |
| for (std::pair<llama_token, int> token_count_primary : part_primary) { | |
| const llama_token token = token_count_primary.first; | |
| common_ngram_cache_part::iterator token_count_static_it = part_static.find(token); | |
| const int32_t count_primary = token_count_primary.second; | |
| const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1; | |
| if (count_primary*count_static > max_count_primary*max_count_static) { | |
| max_token = token; | |
| max_count_primary = count_primary; | |
| max_count_static = count_static; | |
| } | |
| sum_count_primary += count_primary; | |
| } | |
| if (sum_count_primary < min_sample_size[i]) { | |
| continue; | |
| } | |
| if (100*max_count_primary < min_percent[i]*sum_count_primary) { | |
| continue;; | |
| } | |
| drafted_token = max_token; | |
| } | |
| return drafted_token; | |
| } | |
| void common_ngram_cache_draft( | |
| std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max, | |
| common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static | |
| ) { | |
| GGML_ASSERT(draft.size() == 1); | |
| const int inp_size = inp.size(); | |
| if (inp_size < LLAMA_NGRAM_STATIC) { | |
| return; | |
| } | |
| while ((int) draft.size()-1 < n_draft) { | |
| llama_token drafted_token = LLAMA_TOKEN_NULL; | |
| const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; | |
| common_ngram ngram_static; | |
| for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) { | |
| ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j); | |
| } | |
| common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); | |
| common_ngram_cache_part part_static; | |
| if (part_static_it != nc_static.end()) { | |
| part_static = part_static_it->second; | |
| } | |
| // cd = context + dynamic | |
| std::vector<common_ngram> ngrams_cd; | |
| for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) { | |
| const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1; | |
| common_ngram ngram_cd; | |
| for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) { | |
| ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j); | |
| } | |
| ngrams_cd.push_back(ngram_cd); | |
| } | |
| if (drafted_token == LLAMA_TOKEN_NULL) { | |
| drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); | |
| } | |
| if (drafted_token == LLAMA_TOKEN_NULL) { | |
| drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); | |
| } | |
| if (drafted_token == LLAMA_TOKEN_NULL) { | |
| drafted_token = try_draft(nc_static, ngram_static); | |
| } | |
| if (drafted_token == LLAMA_TOKEN_NULL) { | |
| break; | |
| } | |
| LOG_DBG(" - draft candidate: token=%d\n", drafted_token); | |
| draft.push_back(drafted_token); | |
| } | |
| } | |
| void common_ngram_cache_save(common_ngram_cache & ngram_cache, const std::string & filename) { | |
| std::ofstream file_out(filename, std::ios::binary); | |
| for (std::pair<common_ngram, common_ngram_cache_part> item : ngram_cache) { | |
| const common_ngram ngram = item.first; | |
| common_ngram_cache_part token_counts = item.second; | |
| GGML_ASSERT(!token_counts.empty()); | |
| const int32_t ntokens = token_counts.size(); | |
| GGML_ASSERT(ntokens > 0); | |
| file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(common_ngram)); | |
| file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t)); | |
| for (std::pair<llama_token, int32_t> item2 : token_counts) { | |
| const llama_token token = item2.first; | |
| const int32_t count = item2.second; | |
| GGML_ASSERT(count > 0); | |
| file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token)); | |
| file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t)); | |
| } | |
| } | |
| } | |
| common_ngram_cache common_ngram_cache_load(const std::string & filename) { | |
| std::ifstream hashmap_file(filename, std::ios::binary); | |
| if (!hashmap_file) { | |
| throw std::ifstream::failure("Unable to open file " + filename); | |
| } | |
| common_ngram_cache ngram_cache; | |
| common_ngram ngram; | |
| int32_t ntokens; | |
| llama_token token; | |
| int32_t count; | |
| char * ngramc = reinterpret_cast<char*>(&ngram); | |
| char * ntokensc = reinterpret_cast<char*>(&ntokens); | |
| char * tokenc = reinterpret_cast<char*>(&token); | |
| char * countc = reinterpret_cast<char*>(&count); | |
| while(hashmap_file.read(ngramc, sizeof(common_ngram))) { | |
| GGML_ASSERT(!hashmap_file.eof()); | |
| GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t))); | |
| GGML_ASSERT(ntokens > 0); | |
| common_ngram_cache_part token_counts; | |
| for (int i = 0; i < ntokens; ++i) { | |
| GGML_ASSERT(!hashmap_file.eof()); | |
| GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token))); | |
| GGML_ASSERT(!hashmap_file.eof()); | |
| GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t))); | |
| GGML_ASSERT(count > 0); | |
| token_counts.emplace(token, count); | |
| } | |
| ngram_cache.emplace(ngram, token_counts); | |
| } | |
| GGML_ASSERT(hashmap_file.eof()); | |
| return ngram_cache; | |
| } | |
| void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) { | |
| for (std::pair<common_ngram, common_ngram_cache_part> ngram_part : ngram_cache_add) { | |
| const common_ngram ngram = ngram_part.first; | |
| common_ngram_cache_part part = ngram_part.second; | |
| common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram); | |
| if (part_merged_it == ngram_cache_target.end()) { | |
| ngram_cache_target.emplace(ngram, part); | |
| continue; | |
| } | |
| for (std::pair<llama_token, int32_t> token_count : part) { | |
| const llama_token token = token_count.first; | |
| const int32_t count = token_count.second; | |
| GGML_ASSERT(count > 0); | |
| common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token); | |
| if (token_count_merged_it == part_merged_it->second.end()) { | |
| part_merged_it->second.emplace(token, count); | |
| continue; | |
| } | |
| token_count_merged_it->second += count; | |
| } | |
| } | |
| } | |