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
- 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: 1,763 Bytes
76513ec | 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 | #pragma once
#include <optional>
#include <string>
#include <vector>
#include "utils.h"
namespace jinja {
// allow differentiate between user input strings and template strings
// transformations should handle this information as follows:
// - one-to-one (e.g., uppercase, lowercase): preserve is_input flag
// - one-to-many (e.g., strip): if input string is marked as is_input, all resulting parts should be marked as is_input
// - many-to-one (e.g., concat): if ALL input parts are marked as is_input, resulting part should be marked as is_input
struct string_part {
bool is_input = false; // may skip parsing special tokens if true
std::string val;
bool is_uppercase() const;
bool is_lowercase() const;
};
struct string {
std::vector<string_part> parts;
string() = default;
string(const std::string & v, bool user_input = false) {
parts.push_back({user_input, v});
}
string(int v) {
parts.push_back({false, std::to_string(v)});
}
string(double v) {
parts.push_back({false, std::to_string(v)});
}
// mark all parts as user input
void mark_input();
std::string str() const;
size_t length() const;
void hash_update(hasher & hash) const noexcept;
bool all_parts_are_input() const;
bool is_uppercase() const;
bool is_lowercase() const;
// mark this string as input if other has ALL parts as input
void mark_input_based_on(const string & other);
string append(const string & other);
// in-place transformations
string uppercase();
string lowercase();
string capitalize();
string titlecase();
string strip(bool left, bool right, std::optional<const std::string_view> chars = std::nullopt);
};
} // namespace jinja
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