Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

TdAI / llama.cpp /common /chat-auto-parser-helpers.h

tda45

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	#pragma once

	#include "chat-auto-parser.h"

	#include <functional>
	#include <optional>
	#include <string>

	std::string trim_whitespace(const std::string & str);
	std::string trim_leading_whitespace(const std::string & str);
	std::string trim_trailing_whitespace(const std::string & str);
	std::string trim_trailing_newlines(const std::string & str);

	// calculate a diff split (longest common prefix, longest common suffix excluding prefix,
	// mismatched part on the left, mismatched part on the right) between two strings
	// account for markers - align prefix and suffix endings so that they end on markers
	// * eg.:
	// calculate_diff_split("<html><body><div></div></body></html>", "<html><body><p>Something</p></body><html>") ->
	// { "prefix": "<html><body>" (not: "<html><body><"), "suffix": "</body></html>", "left": "<div></div>", "right": "<p>Something</p>" }
	// calculate_diff_split("<html><body>Something</body></html>", "<html><body></body><html>") ->
	// { "prefix": "<html><body>", "suffix": "</body></html>", "left": "Something", "right": "" }
	diff_split calculate_diff_split(const std::string & left, const std::string & right);

	// Returns the prefix of `full` up until the first occurrence of the common prefix of `left` and `right`
	// Returns empty string if there's no common prefix
	// * eg.:
	// until_common_prefix("really want a FUNCTION call", "FUNCTION alpha", "FUNCTION beta") -> "really want a "
	// until_common_prefix("<tool_call>", "<something>", "<something_else>") -> ""
	// until_common_prefix("some text", "1234", "abcd") -> ""
	// until_common_prefix("one arg two args three args four", "argument alpha", "argument beta") -> "one ""
	std::string until_common_prefix(const std::string & full, const std::string & left, const std::string & right);

	// Returns the suffix of `full` after the last occurrence of the common suffix of `left` and `right`
	// Returns empty string if there's no common suffix
	// Mirror function of `until_common_prefix`
	// * eg.:
	// after_common_suffix("really want a FUNCTION call", "first FUNCTION", "second FUNCTION") -> " call"
	// after_common_suffix("one arg two-args three args four", "alpha-args", "beta-args") -> " three args four"
	std::string after_common_suffix(const std::string & full, const std::string & left, const std::string & right);

	// Segmentize text into markers and non-marker fragments
	// * eg.:
	// segmentize_markers("<html><head><title>The site title</title><body><div>Here's some <b>content</b></div></body></html>" ->
	// [ (MARKER, "<html>"), (MARKER, "<head>"), (MARKER, "<title>"), (TEXT, "The site title"), (MARKER, "</title>"),
	// (MARKER, "<body>"), (MARKER, "<div>"), (TEXT, "Here's some "), (MARKER, "<b>"), (TEXT, "content"), (MARKER, "</b>"),
	// (MARKER, "</div>"), (MARKER, "</body>"), (MARKER, "</html>")
	// ]
	// segmentize_markers("<\|tool_call\|>[args]{ are here }[/args]<\|tool_call_end\|>") ->
	// [ (MARKER, "<\|tool_call\|>"), (MARKER, "[args]"), (TEXT, "{ are here }"), (MARKER, "[/args]"), (MARKER, "<\|tool_call_end\|>") ]
	std::vector<segment> segmentize_markers(const std::string & text);

	// Prune whitespace-only segments from a vector of segments
	// * eg.:
	// segmentize_markers("<tool_call>\n<function=foo>\n<arg=bar>\n \n</arg>\n</function>\n</tool_call>") ->
	// X = [ (MARKER, "<tool_call>"), (TEXT, "\n"), (MARKER, "<function=foo>"), (TEXT, "\n"), (MARKER, "<arg=bar>"), (TEXT, "\n \n"),
	// (MARKER, "</arg>"), (TEXT, "\n"), (MARKER, "</function>"), (TEXT, "\n"), (MARKER, "</tool_call>") ]
	// prune_whitespace_segments(X) -> [ (MARKER, "<tool_call>"), (MARKER, "<function=foo>"), (MARKER, "<arg=bar>"), (MARKER, "</arg>"),
	// (MARKER, "</function>"), (MARKER, "</tool_call>") ]
	std::vector<segment> prune_whitespace_segments(const std::vector<segment> & segments);

	namespace autoparser {

	// Apply a template with the given parameters, returning the rendered string (empty on failure)
	std::string apply_template(const common_chat_template & tmpl, const template_params & params);

	// Factorized differential comparison function
	// Takes base params and a single modifier lambda to create variant B
	// Returns compare_variants_result containing diff and both outputs, or std::nullopt on failure
	std::optional<compare_variants_result> compare_variants(
	const common_chat_template & tmpl,
	const template_params & params_A,
	const std::function<void(template_params &)> & params_modifier);

	} // namespace autoparser