python src/transformers/models/llama/convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py at main · huggingface/transformers
import torchfrom transformers import LlamaForCausalLM, LlamaTokenizerfrom peft import PeftModel, PeftConfig 加载分词器和模型: model_id="./models_hf/7B"tokenizer = LlamaTokenizer.from_pretrained(model_id)model =LlamaForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map='auto', tor...
from_pretrained(hf_model_id) return config.to_dict() if __name__ == '__main__': args = parse_args() logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) ftype_map: dict[str, gguf.LlamaFileType] = { "f32": gguf.LlamaFileType.ALL_F32, "f16": ...
logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>") sys.exit(1) else: logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>") sys.exit(1) # TODO: add models here, base models preferred models = [ {"name": "llama-spm",...
Describe the bug In order to convert llama model python convert_llama_weights_to_hf.py --input_dir models/llama-7b --model_size 7B --output_dir models/llama-7b-out which results in NameError: name 'false' is not defined. Did you mean: 'F...
LLM inference in C/C++. Contribute to ggerganov/llama.cpp development by creating an account on GitHub.
HfVocab: "llama", BpeVocab: "gpt2", }.get(type(vocab)) # Block if vocab type is not predefined if tokenizer_model is None: raise ValueError("Unknown vocab type: Not supported") return tokenizer_model def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes...
--loader llama2_hf --saver megatron --target-tensor-parallel-size 1 --target-pipeline-parallel-size 1 --params-dtype bf16 --add-qkv-bias --load-dir /huaqiyun/models/qwen/Qwen1___5-1___8B-Chat/ --save-dir /huaqiyun/models/huawei_ Megatron/Qwen15-18B-Chat/ ...
(nbits - 1)] -= 2**nbits return data.to(get_int_dtype(nbits)) def get_num_layers(config) -> int: match config.model_type: case "llama" | "mistral" | "mixtral": return config.num_hidden_layers case unknown_type: raise NotImplementedError(f"Can't get number of layers for {...