Upload folder using huggingface_hub (#3)
Browse files- 32b5ff6e6667cb8148fd5a5fff4de0dbb3f0df88e9a59dbe77bd70f731fb2140 (6b1b159e75d9cbe09d4d95c60b0516f0ddd1452d)
- 930ae4a8f55e63b7a731e09c60aa3fb7a80c8dd0ed034f4cb12d79aeb5f0e8db (ddf6d3c1bb3cba6b71fb18caf3023160a7b6029a)
- README.md +8 -7
- added_tokens.json +4 -0
- config.json +5 -2
- generation_config.json +1 -1
- smash_config.json +9 -5
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +62 -0
README.md
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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metrics:
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- memory_disk
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- memory_inference
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with llm-int8.
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- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
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- ***How is the model efficiency evaluated?*** These results were obtained on
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- ***What is the model format?*** We use safetensors.
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- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
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- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphin-2.1-mistral-7b")
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-
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```
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## Configurations
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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base_model: cognitivecomputations/dolphin-2.1-mistral-7b
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metrics:
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- memory_disk
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- memory_inference
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with llm-int8.
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- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
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- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
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- ***What is the model format?*** We use safetensors.
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- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
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- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("PrunaAI/cognitivecomputations-dolphin-2.1-mistral-7b-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
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tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphin-2.1-mistral-7b")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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outputs = model.generate(input_ids, max_new_tokens=216)
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tokenizer.decode(outputs[0])
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```
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## Configurations
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added_tokens.json
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{
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"<|im_end|>": 32000,
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"<|im_start|>": 32001
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}
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config.json
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{
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"_name_or_path": "/
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"architectures": [
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"MistralForCausalLM"
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],
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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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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant": false,
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"llm_int8_enable_fp32_cpu_offload": false,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 32002
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}
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{
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"_name_or_path": "/ceph/hdd/staff/charpent/.cache/modelsvhrm_mkd1ao49c9j",
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"architectures": [
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"MistralForCausalLM"
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],
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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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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant": false,
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"llm_int8_enable_fp32_cpu_offload": false,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 32002
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.
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}
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.40.0"
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}
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smash_config.json
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"verify_url": "http://johnrachwan.pythonanywhere.com",
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"smash_config": {
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"pruners": "None",
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"factorizers": "None",
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"quantizers": "['llm-int8']",
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"compilers": "None",
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"
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/
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"batch_size": 1,
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"model_name": "cognitivecomputations/dolphin-2.1-mistral-7b",
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"
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"n_quantization_bits": 4,
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"output_deviation": 0.005,
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"max_batch_size": 1,
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"qtype_weight": "torch.qint8",
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"qtype_activation": "torch.quint8",
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"verify_url": "http://johnrachwan.pythonanywhere.com",
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"smash_config": {
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"pruners": "None",
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"pruning_ratio": 0.0,
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"factorizers": "None",
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"quantizers": "['llm-int8']",
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"weight_quantization_bits": 4,
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"output_deviation": 0.005,
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"compilers": "None",
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"static_batch": true,
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"static_shape": true,
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"controlnet": "None",
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"unet_dim": 4,
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsvhrm_mkd",
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"batch_size": 1,
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"model_name": "cognitivecomputations/dolphin-2.1-mistral-7b",
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"task": "text_text_generation",
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"max_batch_size": 1,
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"qtype_weight": "torch.qint8",
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"qtype_activation": "torch.quint8",
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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": true,
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"normalized": false,
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"rstrip": true,
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"single_word": false
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},
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"eos_token": {
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"content": "<|im_end|>",
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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": true,
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"normalized": false,
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"rstrip": true,
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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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"add_prefix_space": true,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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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": true,
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"normalized": false,
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"rstrip": true,
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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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"32000": {
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"content": "<|im_end|>",
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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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"32001": {
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"content": "<|im_start|>",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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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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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"legacy": false,
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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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"trust_remote_code": false,
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"unk_token": "<unk>",
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"use_default_system_prompt": true,
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"use_fast": true
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}
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