neuralmagic

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Meta-Llama-3.1-8B-Instruct-quantized.w8a16

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I'm trying to load the model using the suggested code:

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16")
pipe(messages)

When I run it, I get a warning that basically none of the wights were used when initializing LlamaForCausalLM. As a result, the generated response (see below) is a bit odd. I'm using transformers version 4.44.2.

2024-08-29 10:40:44.019913: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-08-29 10:40:44.025049: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-08-29 10:40:44.089174: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-08-29 10:40:45.296907: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Loading checkpoint shards: 100%
2/2 [00:01<00:00, 1.54it/s]

Some weights of the model checkpoint at neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16 were not used when initializing LlamaForCausalLM: ['model.layers.0.mlp.down_proj.weight_packed', 'model.layers.0.mlp.down_proj.weight_scale', 'model.layers.0.mlp.down_proj.weight_shape', 'model.layers.0.mlp.gate_proj.weight_packed', 'model.layers.0.mlp.gate_proj.weight_scale', 'model.layers.0.mlp.gate_proj.weight_shape', 'model.layers.0.mlp.up_proj.weight_packed', 'model.layers.0.mlp.up_proj.weight_scale', 'model.layers.0.mlp.up_proj.weight_shape', 'model.layers.0.self_attn.k_proj.weight_packed', 'model.layers.0.self_attn.k_proj.weight_scale', 'model.layers.0.self_attn.k_proj.weight_shape', 'model.layers.0.self_attn.o_proj.weight_packed', 'model.layers.0.self_attn.o_proj.weight_scale', 'model.layers.0.self_attn.o_proj.weight_shape', 'model.layers.0.self_attn.q_proj.weight_packed', 'model.layers.0.self_attn.q_proj.weight_scale', 'model.layers.0.self_attn.q_proj.weight_shape', 'model.layers.0.self_attn.v_proj.weight_packed', 'model.layers.0.self_attn.v_proj.weight_scale', 'model.layers.0.self_attn.v_proj.weight_shape', 'model.layers.1.mlp.down_proj.weight_packed', 'model.layers.1.mlp.down_proj.weight_scale', 'model.layers.1.mlp.down_proj.weight_shape', 'model.layers.1.mlp.gate_proj.weight_packed', 'model.layers.1.mlp.gate_proj.weight_scale', 'model.layers.1.mlp.gate_proj.weight_shape', 'model.layers.1.mlp.up_proj.weight_packed', 'model.layers.1.mlp.up_proj.weight_scale', 'model.layers.1.mlp.up_proj.weight_shape', 'model.layers.1.self_attn.k_proj.weight_packed', 'model.layers.1.self_attn.k_proj.weight_scale', 'model.layers.1.self_attn.k_proj.weight_shape', 'model.layers.1.self_attn.o_proj.weight_packed', 'model.layers.1.self_attn.o_proj.weight_scale', 'model.layers.1.self_attn.o_proj.weight_shape', 'model.layers.1.self_attn.q_proj.weight_packed', 'model.layers.1.self_attn.q_proj.weight_scale', 'model.layers.1.self_attn.q_proj.weight_shape', 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- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of LlamaForCausalLM were not initialized from the model checkpoint at neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16 and are newly initialized: ['model.layers.0.mlp.down_proj.weight', 'model.layers.0.mlp.gate_proj.weight', 'model.layers.0.mlp.up_proj.weight', 'model.layers.0.self_attn.k_proj.weight', 'model.layers.0.self_attn.o_proj.weight', 'model.layers.0.self_attn.q_proj.weight', 'model.layers.0.self_attn.v_proj.weight', 'model.layers.1.mlp.down_proj.weight', 'model.layers.1.mlp.gate_proj.weight', 'model.layers.1.mlp.up_proj.weight', 'model.layers.1.self_attn.k_proj.weight', 'model.layers.1.self_attn.o_proj.weight', 'model.layers.1.self_attn.q_proj.weight', 'model.layers.1.self_attn.v_proj.weight', 'model.layers.10.mlp.down_proj.weight', 'model.layers.10.mlp.gate_proj.weight', 'model.layers.10.mlp.up_proj.weight', 'model.layers.10.self_attn.k_proj.weight', 'model.layers.10.self_attn.o_proj.weight', 'model.layers.10.self_attn.q_proj.weight', 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You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/local_disk0/.ephemeral_nfs/envs/pythonEnv-d6271344-6884-46e7-9edc-d3d2a32c6cc0/lib/python3.11/site-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(

[{'generated_text': [{'role': 'user', 'content': 'Who are you?'},
   {'role': 'assistant', 'content': 'zierzierrens Otihnонь'}]}]