first commit
Browse files- README.md +96 -3
- config.json +45 -0
- model.safetensors +3 -0
- quantize_config.json +19 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
README.md
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## Model Details: Mistral-7B-v0.1-int4-inc
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This model is an int4 model with group_size 128 of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generated by [intel/auto-round](https://github.com/intel/auto-round).
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## How To Use
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### Reproduce the model
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Here is the sample command to reproduce the model
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```bash
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git clone https://github.com/intel/auto-round
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cd auto-round/examples/language-modeling
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pip install -r requirements.txt
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python3 main.py \
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--model_name mistralai/Mistral-7B-v0.1 \
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--device 0 \
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--group_size 128 \
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--bits 4 \
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--iters 1000 \
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--enable_minmax_tuning \
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--use_quant_input \
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--deployment_device 'gpu' \
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--scale_dtype 'fp32' \
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--eval_bs 32 \
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--output_dir "./tmp_autoround" \
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--amp
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```
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### Use the model
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### INT4 Inference with AutoGPTQ
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Install [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) from source first
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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quantized_model_dir = "Intel/Mistral-7B-v0.1-int4-inc"
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model = AutoModelForCausalLM.from_pretrained(quantized_model_dir,
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device_map="auto",
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trust_remote_code=False,
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)
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tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=True)
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print(tokenizer.decode(model.generate(**tokenizer("There is a girl who likes adventure,", return_tensors="pt").to(model.device),max_new_tokens=50)[0]))
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```
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### Evaluate the model
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Install [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness.git) from source, we used the git id f3b7917091afba325af3980a35d8a6dcba03dc3f
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~~bash
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lm_eval --model hf --model_args pretrained="Intel/Mistral-7B-v0.1-int4-inc",autogptq=True,gptq_use_triton=True --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,rte,arc_easy,arc_challenge,mmlu --batch_size 128
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~~
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| Metric | FP16 | INT4 |
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| -------------- | ------ | ------ |
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| Avg. | 0.6306 | 0.6308 |
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| mmlu | 0.5961 | 0.5880 |
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| lambada_openai | 0.7561 | 0.7551 |
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| hellaswag | 0.6128 | 0.6079 |
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| winogrande | 0.7443 | 0.7451 |
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| piqa | 0.8079 | 0.8014 |
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| truthfulqa_mc1 | 0.2803 | 0.2889 |
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| openbookqa | 0.3280 | 0.3300 |
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| boolq | 0.8373 | 0.8278 |
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| rte | 0.6643 | 0.6968 |
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| arc_easy | 0.8085 | 0.8060 |
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| arc_challenge | 0.5009 | 0.4915 |
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## Ethical Considerations and Limitations
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The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
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Therefore, before deploying any applications of the model, developers should perform safety testing.
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## Caveats and Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
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Here are a couple of useful links to learn more about Intel's AI software:
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* Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
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* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
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## Disclaimer
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
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config.json
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{
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"_name_or_path": "/models/Mistral-7B-v0.1",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"quantization_config": {
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"bits": 4,
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"damp_percent": 0.01,
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"desc_act": false,
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"enable_minmax_tuning": true,
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"group_size": 128,
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"is_marlin_format": false,
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"iters": 1000,
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"lr": 0.001,
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"minmax_lr": 0.001,
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"model_file_base_name": "model",
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"model_name_or_path": null,
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"quant_method": "gptq",
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"static_groups": false,
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"sym": false,
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"true_sequential": false,
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"use_quant_input": true,
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"version": "0.1"
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},
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:31dc412aabad01e77ad6526794355a107ebacc50abda9b2f17d25e8a33413dfb
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size 4158662312
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quantize_config.json
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{
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"bits": 4,
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"group_size": 128,
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"damp_percent": 0.01,
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"desc_act": false,
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"static_groups": false,
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"sym": false,
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"true_sequential": false,
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"model_name_or_path": null,
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"model_file_base_name": "model",
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"is_marlin_format": false,
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"quant_method": "intel/auto-round",
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"version": "0.1",
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"iters": 1000,
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"lr": 0.001,
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"minmax_lr": 0.001,
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"enable_minmax_tuning": true,
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"use_quant_input": true
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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size 493443
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [],
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": null,
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": true
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}
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