Text Generation
Transformers
Safetensors
mixtral
conversational
text-generation-inference
4-bit precision
gptq
Instructions to use DSAiLab/KoMultiGen-general-gptq-4bit-32g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSAiLab/KoMultiGen-general-gptq-4bit-32g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DSAiLab/KoMultiGen-general-gptq-4bit-32g") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DSAiLab/KoMultiGen-general-gptq-4bit-32g") model = AutoModelForCausalLM.from_pretrained("DSAiLab/KoMultiGen-general-gptq-4bit-32g", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DSAiLab/KoMultiGen-general-gptq-4bit-32g with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DSAiLab/KoMultiGen-general-gptq-4bit-32g" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DSAiLab/KoMultiGen-general-gptq-4bit-32g", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DSAiLab/KoMultiGen-general-gptq-4bit-32g
- SGLang
How to use DSAiLab/KoMultiGen-general-gptq-4bit-32g with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "DSAiLab/KoMultiGen-general-gptq-4bit-32g" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DSAiLab/KoMultiGen-general-gptq-4bit-32g", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "DSAiLab/KoMultiGen-general-gptq-4bit-32g" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DSAiLab/KoMultiGen-general-gptq-4bit-32g", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DSAiLab/KoMultiGen-general-gptq-4bit-32g with Docker Model Runner:
docker model run hf.co/DSAiLab/KoMultiGen-general-gptq-4bit-32g
Upload folder using huggingface_hub
Browse files- .ipynb_checkpoints/config-checkpoint.json +43 -0
- config.json +43 -0
- model.safetensors +3 -0
- quantize_config.json +13 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +46 -0
.ipynb_checkpoints/config-checkpoint.json
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{
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"_name_or_path": "maywell/KoMultiGen-General",
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"architectures": [
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"MixtralForCausalLM"
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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": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_local_experts": 8,
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"output_router_logits": true,
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"quantization_config": {
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"bits": 3,
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"damp_percent": 0.01,
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"desc_act": true,
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"group_size": 32,
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"is_marlin_format": false,
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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": true,
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"true_sequential": true
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},
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"router_aux_loss_coef": 0.02,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": false,
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"vocab_size": 32000
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}
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config.json
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{
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"_name_or_path": "maywell/KoMultiGen-General",
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"architectures": [
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"MixtralForCausalLM"
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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": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_local_experts": 8,
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"output_router_logits": true,
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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": true,
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"group_size": 32,
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"is_marlin_format": false,
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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": true,
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"true_sequential": true
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},
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"router_aux_loss_coef": 0.02,
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"sliding_window": null,
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| 38 |
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"tie_word_embeddings": false,
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| 39 |
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": false,
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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 sha256:446362cf625f00acfed4fb8dd9095eb6aa39a39bd455862ad35a6fa521f83090
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size 27417835256
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quantize_config.json
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{
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"bits": 4,
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"group_size": 32,
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"damp_percent": 0.01,
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"desc_act": true,
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"static_groups": false,
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"sym": true,
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"true_sequential": true,
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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": "gptq"
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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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"pad_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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"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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| 8 |
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"lstrip": false,
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| 9 |
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"normalized": false,
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"rstrip": false,
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| 11 |
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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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| 18 |
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"rstrip": false,
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| 19 |
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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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| 25 |
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"normalized": false,
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| 26 |
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"rstrip": false,
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| 27 |
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"single_word": false,
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"special": true
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| 29 |
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}
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},
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"additional_special_tokens": [],
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| 32 |
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"bos_token": "<s>",
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| 33 |
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"chat_template": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{% for message in messages %}{% if message['role'] == 'user' %}### Instruction:\n{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% elif message['role'] == 'assistant' %}### Response:\n{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% elif message['role'] == 'system' %}{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% endif %}\n{% endfor %}\n{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}\n### Response:\n{% endif %}",
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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": "</s>",
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"sp_model_kwargs": {},
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| 40 |
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"trust_remote_code": true,
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"unk_token": "<unk>",
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| 44 |
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"use_default_system_prompt": false,
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"use_fast": true
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}
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