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: 2,476 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 78 79 80 81 82 83 84 85 | #!/usr/bin/env python3
import logging
import os
import hashlib
logger = logging.getLogger("verify-checksum-models")
def sha256sum(file):
block_size = 16 * 1024 * 1024 # 16 MB block size
b = bytearray(block_size)
file_hash = hashlib.sha256()
mv = memoryview(b)
with open(file, 'rb', buffering=0) as f:
while True:
n = f.readinto(mv)
if not n:
break
file_hash.update(mv[:n])
return file_hash.hexdigest()
# Define the path to the llama directory (parent folder of script directory)
llama_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
# Define the file with the list of hashes and filenames
hash_list_file = os.path.join(llama_path, "SHA256SUMS")
# Check if the hash list file exists
if not os.path.exists(hash_list_file):
logger.error(f"Hash list file not found: {hash_list_file}")
exit(1)
# Read the hash file content and split it into an array of lines
with open(hash_list_file, "r") as f:
hash_list = f.read().splitlines()
# Create an array to store the results
results = []
# Loop over each line in the hash list
for line in hash_list:
# Split the line into hash and filename
hash_value, filename = line.split(" ")
# Get the full path of the file by joining the llama path and the filename
file_path = os.path.join(llama_path, filename)
# Informing user of the progress of the integrity check
logger.info(f"Verifying the checksum of {file_path}")
# Check if the file exists
if os.path.exists(file_path):
# Calculate the SHA256 checksum of the file using hashlib
file_hash = sha256sum(file_path)
# Compare the file hash with the expected hash
if file_hash == hash_value:
valid_checksum = "V"
file_missing = ""
else:
valid_checksum = ""
file_missing = ""
else:
valid_checksum = ""
file_missing = "X"
# Add the results to the array
results.append({
"filename": filename,
"valid checksum": valid_checksum,
"file missing": file_missing
})
# Print column headers for results table
print("filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20)) # noqa: NP100
print("-" * 80) # noqa: NP100
# Output the results as a table
for r in results:
print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}") # noqa: NP100
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