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Model: AI-ModelScope/Mistral-7B-Instruct-v0.1 Source: Original Platform
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---
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- finetuned
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inference:
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parameters:
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temperature: 0.7
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---
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# Model Card for Mistral-7B-Instruct-v0.1
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The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
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For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
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## Instruction format
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In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
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E.g.
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```
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text = "<s>[INST] What is your favourite condiment? [/INST]"
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"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
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"[INST] Do you have mayonnaise recipes? [/INST]"
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```
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This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
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```python
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# pip install "transformers>=4.34" "modelscope>=1.9.2"
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from modelscope import AutoModelForCausalLM, AutoTokenizer, snapshot_download
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import torch
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device = "cuda" # the device to load the model onto
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model_dir = snapshot_download('AI-ModelScope/Mistral-7B-Instruct-v0.1', revision='v1.0.0')
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model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, device_map=device)
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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messages = [
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{"role": "user", "content": "What is your favourite condiment?"},
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{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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{"role": "user", "content": "Do you have mayonnaise recipes?"}
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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## 微调(SFT)
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**代码链接**: https://github.com/modelscope/swift/tree/main/examples/pytorch/llm
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1. 支持的sft方法: lora, qlora, 全参数微调, ...
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2. 支持的模型: qwen系列, qwen-vl系列, baichuan系列, chatglm2系列, llama系列, openbuddy-llama系列, internlm系列, xverse系列, ...
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3. 支持的特性: 模型量化, DDP, 模型并行, gradient checkpointing, 梯度累加, 支持推送ModelScope Hub, 自定义数据集, 多模态和Agent SFT, 多轮对话, ...
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使用lora+ddp+deepspeed SFT mistral-7b-chat的脚本 (需要2\*21GB显存)
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```bash
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# https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mistral_7b_chat/lora_ddp_ds/sft.sh
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# Experimental environment: 2 * A10
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# 2 * 21GB GPU memory
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nproc_per_node=2
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PYTHONPATH=../../.. \
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CUDA_VISIBLE_DEVICES=0,1 \
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torchrun \
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--nproc_per_node=$nproc_per_node \
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--master_port 29500 \
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src/llm_sft.py \
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--model_type mistral-7b-chat \
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--sft_type lora \
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--template_type llama \
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--dtype bf16 \
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--output_dir output \
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--ddp_backend nccl \
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--dataset leetcode-python-en \
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--train_dataset_sample -1 \
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--num_train_epochs 1 \
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--max_length 4096 \
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--lora_rank 8 \
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--lora_alpha 32 \
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--lora_dropout_p 0. \
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--lora_target_modules ALL \
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--gradient_checkpointing true \
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--batch_size 1 \
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--weight_decay 0. \
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--learning_rate 1e-4 \
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--gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
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--max_grad_norm 0.5 \
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--warmup_ratio 0.03 \
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--eval_steps 100 \
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--save_steps 100 \
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--save_total_limit 2 \
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--logging_steps 10 \
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--push_to_hub false \
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--hub_model_id mistral-7b-chat-qlora \
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--hub_private_repo true \
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--hub_token 'your-sdk-token' \
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--deepspeed_config_path 'ds_config/zero2.json' \
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--only_save_model true \
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```
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## Model Architecture
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This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
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- Grouped-Query Attention
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- Sliding-Window Attention
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- Byte-fallback BPE tokenizer
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## Troubleshooting
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- If you see the following error:
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```
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Traceback (most recent call last):
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File "", line 1, in
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File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
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config, kwargs = AutoConfig.from_pretrained(
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File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
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config_class = CONFIG_MAPPING[config_dict["model_type"]]
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File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
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raise KeyError(key)
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KeyError: 'mistral'
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```
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Installing transformers from source should solve the issue
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pip install git+https://github.com/huggingface/transformers
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This should not be required after transformers-v4.33.4.
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## Limitations
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The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
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It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
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make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
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## The Mistral AI Team
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Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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{
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"architectures": [
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"MistralForCausalLM"
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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": 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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"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": "4.34.0.dev0",
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"use_cache": true,
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"vocab_size": 32000
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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}
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"model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
||||
}
|
||||
}
|
||||
5
special_tokens_map.json
Normal file
5
special_tokens_map.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
91122
tokenizer.json
Normal file
91122
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user