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: 2,866 Bytes
0862f9d | 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 | #!/usr/bin/env python
'''
Fetches the Jinja chat template of a HuggingFace model.
If a model has multiple chat templates, you can specify the variant name.
Syntax:
./scripts/get_chat_template.py model_id [variant]
Examples:
./scripts/get_chat_template.py CohereForAI/c4ai-command-r-plus tool_use
./scripts/get_chat_template.py microsoft/Phi-3.5-mini-instruct
'''
import json
import re
import sys
def get_chat_template(model_id, variant=None):
try:
# Use huggingface_hub library if available.
# Allows access to gated models if the user has access and ran `huggingface-cli login`.
from huggingface_hub import hf_hub_download
with open(hf_hub_download(repo_id=model_id, filename="tokenizer_config.json"), encoding="utf-8") as f:
config_str = f.read()
except ImportError:
import requests
assert re.match(r"^[\w.-]+/[\w.-]+$", model_id), f"Invalid model ID: {model_id}"
response = requests.get(f"https://huggingface.co/{model_id}/resolve/main/tokenizer_config.json")
if response.status_code == 401:
raise Exception('Access to this model is gated, please request access, authenticate with `huggingface-cli login` and make sure to run `pip install huggingface_hub`')
response.raise_for_status()
config_str = response.text
try:
config = json.loads(config_str)
except json.JSONDecodeError:
# Fix https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
# (Remove extra '}' near the end of the file)
config = json.loads(re.sub(r'\}([\n\s]*\}[\n\s]*\],[\n\s]*"clean_up_tokenization_spaces")', r'\1', config_str))
chat_template = config['chat_template']
if isinstance(chat_template, str):
return chat_template
else:
variants = {
ct['name']: ct['template']
for ct in chat_template
}
def format_variants():
return ', '.join(f'"{v}"' for v in variants.keys())
if variant is None:
if 'default' not in variants:
raise Exception(f'Please specify a chat template variant (one of {format_variants()})')
variant = 'default'
sys.stderr.write(f'Note: picked "default" chat template variant (out of {format_variants()})\n')
elif variant not in variants:
raise Exception(f"Variant {variant} not found in chat template (found {format_variants()})")
return variants[variant]
def main(args):
if len(args) < 1:
raise ValueError("Please provide a model ID and an optional variant name")
model_id = args[0]
variant = None if len(args) < 2 else args[1]
template = get_chat_template(model_id, variant)
sys.stdout.write(template)
if __name__ == '__main__':
main(sys.argv[1:])
|