File size: 2,451 Bytes
523f40b | 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 | # Copy this code to Google Colab for model conversion
# =================================================
# Cell 1: Install Unsloth
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
# Cell 2: Load your model
from unsloth import FastLanguageModel
import torch
# Replace 'YOUR_USERNAME/YOUR_REPO_NAME' with your actual HuggingFace repo
model_name = "YOUR_USERNAME/YOUR_REPO_NAME" # CHANGE THIS!
max_seq_length = 2048
dtype = None # None for auto detection
load_in_4bit = True
# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
print("Model loaded successfully!")
print(f"Model type: {model.config.model_type}")
print(f"Model architecture: {model.config.architectures}")
# Cell 3: Check the chat template
print("Current chat template:")
print(repr(tokenizer.chat_template))
# Cell 4: Convert to GGUF for Ollama
print("Starting GGUF conversion...")
# Save as GGUF with Ollama Modelfile
model.save_pretrained_gguf(
"ollama_converted_model",
tokenizer,
quantization_method = "q4_k_m", # Good balance of size and quality
)
print("Conversion completed!")
print("Files created:")
!ls -la ollama_converted_model/
# Cell 5: Download the converted files
from google.colab import files
import zipfile
import os
# Zip the converted model
with zipfile.ZipFile('ollama_model.zip', 'w') as zipf:
for root, dirs, files_list in os.walk('ollama_converted_model'):
for file in files_list:
file_path = os.path.join(root, file)
zipf.write(file_path, os.path.relpath(file_path, 'ollama_converted_model'))
print("Model packaged! Downloading...")
files.download('ollama_model.zip')
# Cell 6: Show Ollama Modelfile content
print("\n=== Ollama Modelfile content ===")
try:
with open('ollama_converted_model/Modelfile', 'r') as f:
modelfile_content = f.read()
print(modelfile_content)
except FileNotFoundError:
print("Modelfile not found. The model might not have Ollama support.")
print("\n=== Instructions ===")
print("1. Download the ollama_model.zip file")
print("2. Extract it on your Windows machine")
print("3. Use 'ollama create your-model-name -f Modelfile' to import")
print("4. Run 'ollama run your-model-name' to test")
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