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
File size: 6,519 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | #pragma once
#include "server-http.h"
#include "server-task.h"
#include "server-queue.h"
#include <nlohmann/json_fwd.hpp>
#include <cstddef>
#include <memory>
#include <set>
struct server_context_impl; // private implementation
struct server_context_meta {
std::string build_info;
std::string model_name;
std::set<std::string> model_aliases;
std::set<std::string> model_tags;
std::string model_path;
bool has_mtmd;
bool has_inp_image;
bool has_inp_audio;
bool has_inp_video;
json json_ui_settings;
int slot_n_ctx;
enum llama_pooling_type pooling_type;
// chat params
server_chat_params & chat_params;
std::map<std::string, bool> chat_template_caps;
// tokens
std::string bos_token_str;
std::string eos_token_str;
llama_token fim_pre_token;
llama_token fim_sub_token;
llama_token fim_mid_token;
llama_token fim_pad_token;
llama_token fim_rep_token;
llama_token fim_sep_token;
// sampling
std::vector<llama_logit_bias> logit_bias_eog;
// model meta
enum llama_vocab_type model_vocab_type;
int32_t model_vocab_n_tokens;
int32_t model_n_ctx_train;
int32_t model_n_embd_inp;
uint64_t model_n_params;
uint64_t model_size;
std::string model_ftype;
};
enum server_state {
SERVER_STATE_DOWNLOADING,
SERVER_STATE_LOADING,
SERVER_STATE_READY,
SERVER_STATE_SLEEPING,
};
static std::string server_state_to_str(server_state state) {
switch (state) {
case SERVER_STATE_DOWNLOADING: return "downloading";
case SERVER_STATE_LOADING: return "loading";
case SERVER_STATE_READY: return "ready";
case SERVER_STATE_SLEEPING: return "sleeping";
default: GGML_ASSERT(false && "invalid server_state");
}
}
static server_state server_state_from_str(const std::string & str) {
if (str == "downloading") return SERVER_STATE_DOWNLOADING;
if (str == "loading") return SERVER_STATE_LOADING;
if (str == "ready") return SERVER_STATE_READY;
if (str == "sleeping") return SERVER_STATE_SLEEPING;
GGML_ASSERT(false && "invalid server_state string");
}
using server_state_callback_t = std::function<void(server_state, json /* payload */)>;
struct server_context {
std::unique_ptr<server_context_impl> impl;
server_context();
~server_context();
// load the model and initialize llama_context
// returns true on success
bool load_model(common_params & params);
// this function will block main thread until termination
void start_loop();
// terminate main loop (will unblock start_loop)
void terminate();
// get the underlaying llama_context, can return nullptr if sleeping
// not thread-safe, should only be used from the main thread
llama_context * get_llama_context() const;
// get a new response reader, used by CLI application
server_response_reader get_response_reader();
// get server metadata (read-only), can only be called after load_model()
// not thread-safe, should only be used from the main thread
server_context_meta get_meta() const;
// note: must be set before load_model() is called
void set_state_callback(server_state_callback_t callback);
};
// forward declarations
struct server_res_generator;
struct server_routes {
server_routes(const common_params & params, server_context & ctx_server);
void init_routes();
// note: this is not thread-safe and can only when ctx_http.is_ready is false
void update_meta(const server_context & ctx_server) {
this->meta = std::make_unique<server_context_meta>(ctx_server.get_meta());
}
// handlers using lambda function, so that they can capture `this` without `std::bind`
// they won't be called until ctx_http.is_ready is set to true
server_http_context::handler_t get_health;
server_http_context::handler_t get_metrics;
server_http_context::handler_t get_slots;
server_http_context::handler_t post_slots;
server_http_context::handler_t get_props;
server_http_context::handler_t post_props;
server_http_context::handler_t post_infill;
server_http_context::handler_t post_completions;
server_http_context::handler_t post_completions_oai;
server_http_context::handler_t post_chat_completions;
server_http_context::handler_t post_chat_completions_tok;
server_http_context::handler_t post_control;
server_http_context::handler_t post_responses_oai;
server_http_context::handler_t post_responses_tok_oai;
server_http_context::handler_t post_transcriptions_oai;
server_http_context::handler_t post_anthropic_messages;
server_http_context::handler_t post_anthropic_count_tokens;
server_http_context::handler_t post_apply_template;
server_http_context::handler_t get_models;
server_http_context::handler_t post_tokenize;
server_http_context::handler_t post_detokenize;
server_http_context::handler_t post_embeddings;
server_http_context::handler_t post_embeddings_oai;
server_http_context::handler_t post_rerank;
server_http_context::handler_t get_lora_adapters;
server_http_context::handler_t post_lora_adapters;
// to be used in router mode
json get_model_info() const;
private:
std::unique_ptr<server_res_generator> handle_completions_impl(
const server_http_req & req,
server_task_type type,
const json & data,
const std::vector<raw_buffer> & files,
task_response_type res_type);
std::unique_ptr<server_res_generator> handle_slots_save(const server_http_req & req, int id_slot);
std::unique_ptr<server_res_generator> handle_slots_restore(const server_http_req & req, int id_slot);
std::unique_ptr<server_res_generator> handle_slots_erase(const server_http_req &, int id_slot);
std::unique_ptr<server_res_generator> handle_embeddings_impl(const server_http_req & req, task_response_type res_type);
std::unique_ptr<server_res_generator> handle_count_tokens(const llama_vocab * vocab, mtmd_context * mctx, const server_http_req & req, task_response_type res_type);
// using unique_ptr to allow late initialization of const
std::unique_ptr<const server_context_meta> meta;
const common_params & params;
const server_context_impl & ctx_server;
server_queue & queue_tasks;
server_response & queue_results;
std::unique_ptr<server_res_generator> create_response(bool bypass_sleep = false);
};
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