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Meta-Llama-3.1-8B-Instruct-bnb-4bit

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Unsloth AI 571

in the config rope settings are wrong and from the older model. new model has a larger max token which is not correctly set. additionally, there is this error which asks for only two values in the rope config:
ValueError: rope_scaling must be a dictionary with two fields, type and factor, got {'factor': 8.0, 'high_freq_factor': 4.0, 'low_freq_factor': 1.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}

Thank you for your input. Have you found the correct way to fix the config?

@RaccoonOnion I think config must be changed. but I'm not sure what settings should be used, I just know original_max_position_embeddings': 8192 is wrong and it must be 131072 or 128000, because the new models have extended context to 128k.

Not with unsloth, but I've run into the same issue with tryign to convert the new 3.1 model to a GPTQ.
I was able to pop into the downloaded model config.json file. Manually edit the settings and run with success. Same might apply here?

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": [
    128001,
    128008,
    128009
  ],
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 131072,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": {
    "factor": 8.0,
    "type": "linear"
  },
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.42.3",
  "use_cache": true,
  "vocab_size": 128256
}

@thesven yup! I think this is the correct config! Thank you so much kind sir!

@Maani @thesven You should update transformers lib to 4.43.1 since they addressed new rope config there, the error is solved for me after that

@djalexj is right. I can confirm that upgrading to transformers 4.43.1 solves the issue. it's updated 2 hrs ago btw. lol.
just use

!pip install transformers==4.43.1

I close this conversation now, the issue is resolved.