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: 3,395 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 | import pytest
import requests
from utils import *
server = ServerPreset.tinyllama2()
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
def test_server_start_simple():
global server
server.start()
res = server.make_request("GET", "/health")
assert res.status_code == 200
def test_server_props():
global server
server.start()
res = server.make_request("GET", "/props")
assert res.status_code == 200
assert ".gguf" in res.body["model_path"]
assert res.body["total_slots"] == server.n_slots
default_val = res.body["default_generation_settings"]
assert server.n_ctx is not None and server.n_slots is not None
assert default_val["n_ctx"] == server.n_ctx / server.n_slots
assert default_val["params"]["seed"] == server.seed
def test_server_models():
global server
server.start()
res = server.make_request("GET", "/models")
assert res.status_code == 200
assert len(res.body["data"]) == 1
assert res.body["data"][0]["id"] == server.model_alias
def test_server_slots():
global server
# without slots endpoint enabled, this should return error
server.server_slots = False
server.start()
res = server.make_request("GET", "/slots")
assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED
assert "error" in res.body
server.stop()
# with slots endpoint enabled, this should return slots info
server.server_slots = True
server.n_slots = 2
server.start()
res = server.make_request("GET", "/slots")
assert res.status_code == 200
assert len(res.body) == server.n_slots
assert server.n_ctx is not None and server.n_slots is not None
assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots
assert "params" not in res.body[0]
def test_load_split_model():
global server
server.offline = False
server.model_hf_repo = "ggml-org/models"
server.model_hf_file = "tinyllamas/split/stories15M-q8_0-00001-of-00003.gguf"
server.model_alias = "tinyllama-split"
server.start()
res = server.make_request("POST", "/completion", data={
"n_predict": 16,
"prompt": "Hello",
"temperature": 0.0,
})
assert res.status_code == 200
assert match_regex("(little|girl)+", res.body["content"])
def test_no_ui():
global server
# default: UI enabled
server.start()
url = f"http://{server.server_host}:{server.server_port}"
res = requests.get(url)
assert res.status_code == 200
assert "<!doctype html>" in res.text
server.stop()
# with --no-ui, the UI should be disabled
server.no_ui = True
server.start()
res = requests.get(url)
assert res.status_code == 404
def test_server_model_aliases_and_tags():
global server
server.model_alias = "tinyllama-2,fim,code"
server.model_tags = "chat,fim,small"
server.start()
res = server.make_request("GET", "/models")
assert res.status_code == 200
assert len(res.body["data"]) == 1
model = res.body["data"][0]
# aliases field must contain all aliases
assert set(model["aliases"]) == {"tinyllama-2", "fim", "code"}
# tags field must contain all tags
assert set(model["tags"]) == {"chat", "fim", "small"}
# id is derived from first alias (alphabetical order from std::set)
assert model["id"] == "code"
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