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Browse files- .ipynb_checkpoints/README-checkpoint.md +55 -0
- README.md +55 -0
- config.json +22 -0
- generation_config.json +7 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +33 -0
- vicuna-13B-1.1-Chinese-GPTQ-4bit-128g.safetensors +3 -0
.ipynb_checkpoints/README-checkpoint.md
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# Vicuna 13B V1.1 Chinese
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This model was obtained from following repo:
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* uukuguy/vicuna-13b-v1.1
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* ziqingyang/chinese-alpaca-lora-13b
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Merged using sciprts from: https://github.com/ymcui/Chinese-LLaMA-Alpaca
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Then quanized using following command (no act order):
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```
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python llama.py ~/Chinese-LLaMA-Alpaca/alpaca-combined-hf c4 \
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--wbits 4 \
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--true-sequential \
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--groupsize 128 \
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--save_safetensors vicuna-13B-1.1-Chinese-GPTQ-4bit-128g.safetensors
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```
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Can confirm model output normal text, but question-answering quality is unknown
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# Vicuna Model Card
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## Model details
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**Model type:**
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Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
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It is an auto-regressive language model, based on the transformer architecture.
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**Model date:**
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Vicuna was trained between March 2023 and April 2023.
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**Organizations developing the model:**
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The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
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**Paper or resources for more information:**
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https://vicuna.lmsys.org/
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**License:**
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Apache License 2.0
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**Where to send questions or comments about the model:**
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https://github.com/lm-sys/FastChat/issues
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## Intended use
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**Primary intended uses:**
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The primary use of Vicuna is research on large language models and chatbots.
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**Primary intended users:**
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
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## Training dataset
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70K conversations collected from ShareGPT.com.
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## Evaluation dataset
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A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
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README.md
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# Vicuna 13B V1.1 Chinese
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This model was obtained from following repo:
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* uukuguy/vicuna-13b-v1.1
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* ziqingyang/chinese-alpaca-lora-13b
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Merged using sciprts from: https://github.com/ymcui/Chinese-LLaMA-Alpaca
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Then quanized using following command (no act order):
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```
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python llama.py ~/Chinese-LLaMA-Alpaca/alpaca-combined-hf c4 \
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--wbits 4 \
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--true-sequential \
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--groupsize 128 \
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--save_safetensors vicuna-13B-1.1-Chinese-GPTQ-4bit-128g.safetensors
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```
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Can confirm model output normal text, but question-answering quality is unknown
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# Vicuna Model Card
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## Model details
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**Model type:**
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Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
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+
It is an auto-regressive language model, based on the transformer architecture.
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+
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**Model date:**
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Vicuna was trained between March 2023 and April 2023.
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**Organizations developing the model:**
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The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
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**Paper or resources for more information:**
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https://vicuna.lmsys.org/
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**License:**
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Apache License 2.0
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**Where to send questions or comments about the model:**
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https://github.com/lm-sys/FastChat/issues
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## Intended use
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**Primary intended uses:**
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The primary use of Vicuna is research on large language models and chatbots.
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47 |
+
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48 |
+
**Primary intended users:**
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49 |
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
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+
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## Training dataset
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70K conversations collected from ShareGPT.com.
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+
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## Evaluation dataset
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A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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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": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.28.0",
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"use_cache": true,
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"vocab_size": 49954
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}
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generation_config.json
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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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"pad_token_id": 0,
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"transformers_version": "4.28.0"
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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": true,
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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": true,
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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": true,
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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 sha256:2d967e855b1213a439df6c8ce2791f869c84b4f3b6cfacf22b86440b8192a2f8
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size 757972
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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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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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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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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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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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vicuna-13B-1.1-Chinese-GPTQ-4bit-128g.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d20613286fdb90b7974a199fdf441e579c63f0a4b328df14468cb56e5df877a
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size 7822495082
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