Introduction

I'm currently working on small language models, especially Llama. Since I want to serve the model using LM Studio, I need to convert it into GGUF format

.pth (Meta Checkpoint)
  ↓ convert_llama_weights_to_hf.py
HF directory (config.json, tokenizer.model, .safetensors)
  ↓ convert-hf-to-gguf.py
GGUF file (compatible with llama.cpp / LM Studio)

Requirement

• Clone and build the llama.cpp repository

• Python 3.10 or higher environment (recommended: venv or conda)

• Required packages:

pip install torch numpy sentencepiece transformers huggingface_hub gguf

Llama Datafile

We can download Llama from Class Leading, Open-Source AI | Download Llama For CodeLlama, you can use this link: meta-llama/codellama After finishing the download, you can see these files in the directory - consolidated.pth, consolidated.01.pth, etc. - Model weight data - Sharded (e.g., 13B model has 2 shards, 65B has 8) - .pth contains raw tensor data only, no model architecture - params.json - Model hyperparameter settings (e.g., hidden size, vocab size, num_layers) - Used to generate HF config during conversion - tokenizer.model - SentencePiece tokenizer model (used in all LLaMA variants) - Tokenisation schema - Converted to tokenizer.json, tokenizer_config.json in HF format

PyTorch .pth → Hugging Face Format Conversion

Then change .pth to the Hugging Face format. Because to change .pth to GGUF files, we should extract HF format metadata. Since my disk usage is limited, I also need to save my models in Hugging Face

• Why convert .pth to Hugging Face format? - .pth cannot be directly used with LM Studio or llama.cpp - Hugging Face format is standardised and compatible with formats like GGUF - Separates tokenizer, config, and weights for clearer model management
• In the downloaded directory, use transformers/models/llama/convert_llama_weights_to_hf.py

python convert_llama_weights_to_hf.py \
  --input_dir /path/to/CodeLlama-13B \
  --model_size 13B \
  --output_dir ./hf-codellama13b

This script (within transformers) converts Meta's CodeLlama .pth + tokenizer into Hugging Face sharded .safetensors.

Hugging Face → GGUF Conversion

• Convert the Hugging Face format directory into GGUF:

cd llama.cpp
python llama.cpp/convert_hf_to_gguf.py CodeLlama-13b/output --outfile codellama13b.gguf --outtype f32

• Required files in the HF directory: - config.json, tokenizer.json, tokenizer_config.json - tokenizer.model - Sharded .safetensors files
• Ensure Python dependencies are correctly installed
• --outtype specifies output precision: - f32 (float‑32): - Standard 32-bit floating point - Maximum accuracy; lossless weight reconstruction - Large file and memory footprint - f16 (float‑16): - Half-precision 16-bit - Still lossless if source weights are 16-bit (e.g., BF16) - Smaller size (~50%), lower memory, faster inference

How to Upload Model in to Hugging Face

❯ cd CodeLlama-7b
❯ huggingface-cli upload Berom0227/codellama-7b --type model
huggingface-cli: error: unrecognised arguments: --type model

❯ huggingface-cli repo create Berom0227/codellama-7b --type model
The --type argument is deprecated and will be removed in a future version. Use --repo-type instead.
Successfully created Berom0227/codellama-7b on the Hub.
Your repo is now available at https://huggingface.co/Berom0227/codellama-7b

❯ huggingface-cli upload Berom0227/codellama-7b . --include='*'

Hugging Face CLI Commands Explanation

• huggingface-cli repo create <repo_id> --repo-type model - Creates a new model repository on Hugging Face Hub - --repo-type model specifies the repository type - --type is deprecated and should not be used - Example: huggingface-cli repo create Berom0227/codellama-7b --repo-type model
• huggingface-cli upload <repo_id> <local_path> --include='*' - Uploads local files to the Hugging Face repository - --include='*' includes all files for upload - Example: huggingface-cli upload Berom0227/codellama-7b . --include='*'
• Error examples and solutions: - Error: huggingface-cli: error: unrecognised arguments: --type model - Solution: Use --repo-type model instead of deprecated --type model