Unable to Fine Tuning LLama2 for sequence classification
I’ve been trying to train my own sequence classification model but get this error when trying to do itI’ve been following this write upblog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral.md at main · huggingface/blog · GitHubRuntimeError: mat1 and mat2 shapes cannot be multiplied (416x4096 and 1x8388608)can some one help me solve this.compute_dtype = getattr(torch, “float16”)bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=False,
)llama_model =  AutoModelForSequenceClassification.from_pretrained(
  pretrained_model_name_or_path=llama_checkpoint,
  num_labels=2,
  device_map= device, #"auto",
  offload_folder="offload",
  trust_remote_code=True,
  quantization_config=bnb_config
)Llama configLlamaConfig {
  "_name_or_path": "meta-llama/Llama-2-7b-hf",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "max_position_embeddings": 4096,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 32,
  "pad_token_id": 2,
  "pretraining_tp": 1,
  "quantization_config": {
    "_load_in_4bit": true,
    "_load_in_8bit": false,
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_quant_type": "nf4",
    "bnb_4bit_use_double_quant": false,
    "llm_int8_enable_fp32_cpu_offload": false,
    "llm_int8_has_fp16_weight": false,
    "llm_int8_skip_modules": null,
    "llm_int8_threshold": 6.0,
    "quant_method": "bitsandbytes"
  },
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.38.0.dev0",
  "use_cache": false,
  "vocab_size": 32000
}