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.
- 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
- 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
- Atomic Chat
File size: 4,223 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 | #pragma once
#include "llama-kv-cache.h"
#include <vector>
//
// llama_kv_cache_dsa
//
// utilizes two instances of llama_kv_cache:
// - the first instance is for caching key tensors of the model,
// - the second instance is for caching lightning indexer key tensors
class llama_kv_cache_dsa : public llama_memory_i {
public:
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);
~llama_kv_cache_dsa() = default;
//
// llama_memory_i
//
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_context_ptr init_full() override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
void clear(bool data) override;
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
void seq_keep(llama_seq_id seq_id) override;
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
//
// llama_kv_cache_dsa specific API
//
llama_kv_cache * get_mla() const;
llama_kv_cache * get_lid() const;
private:
// we keep indexer KV cache hparams instance here as llama_kv_cache stores only reference to it
llama_hparams hparams_lid;
const uint32_t n_stream = 1;
std::unique_ptr<llama_kv_cache> kv_mla;
std::unique_ptr<llama_kv_cache> kv_lid;
};
class llama_kv_cache_dsa_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
// used for errors
llama_kv_cache_dsa_context(llama_memory_status status);
// used to create a full-cache context
llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv);
// used to create an update context
llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv,
llama_context * lctx,
bool optimize);
// used to create a batch processing context from a batch
llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_ik,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_dsa_context();
//
// llama_memory_context_i
//
bool next() override;
bool apply() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_dsa_context specific API
//
const llama_kv_cache_context * get_mla() const;
const llama_kv_cache_context * get_lid() const;
private:
//llama_kv_cache_dsa * kv;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_context_ptr ctx_mla;
const llama_memory_context_ptr ctx_lid;
const llama_memory_status status;
};
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