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Model: lamm-mit/BioinspiredZephyr-7B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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---
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### BioinspiredZephyr-7B: Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
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To accelerate discovery and guide insights, we report an open-source autoregressive transformer large language model (LLM), trained on expert knowledge in the biological materials field, especially focused on mechanics and structural properties.
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The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity.
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The model is based on HuggingFaceH4/zephyr-7b-beta.
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This model is based on work reported in https://doi.org/10.1002/advs.202306724.
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This repository includes both, Hugging Face transformers and GGUF files (in different versions, the q5_K_M is recommended).
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#### Hugging Face transformers files: Loading and inference
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import infer_auto_device_map
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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device_map="auto", #device_map="cuda:0",
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torch_dtype= torch.bfloat16,
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# use_flash_attention_2=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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Chat template
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```
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messages = [
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{"role": "system", "content": "You are a friendly materials scientist."},
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{"role": "user", "content": "What is the strongest spider silk material?"},
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{"role": "assistant", "content": "Sample response."},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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```
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'<|system|>\nYou are a friendly materials scientist.</s>\n<|user|>\nWhat is the strongest spider silk material?</s>\n<|assistant|>\nSample response.</s>\n<|assistant|>\n'
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```
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device='cuda'
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def generate_response (text_input="Biological materials offer amazing possibilities, such as",
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num_return_sequences=1,
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temperature=1.,
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max_new_tokens=127,
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num_beams=1,
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top_k = 50,
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top_p =0.9,repetition_penalty=1.,eos_token_id=2,verbatim=False,
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exponential_decay_length_penalty_fac=None,
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):
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inputs = tokenizer.encode(text_input, add_special_tokens =False, return_tensors ='pt')
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if verbatim:
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print ("Length of input, tokenized: ", inputs.shape, inputs)
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with torch.no_grad():
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outputs = model.generate(input_ids=inputs.to(device),
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max_new_tokens=max_new_tokens,
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temperature=temperature, #value used to modulate the next token probabilities.
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num_beams=num_beams,
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top_k = top_k,
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top_p =top_p,
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num_return_sequences = num_return_sequences, eos_token_id=eos_token_id,
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do_sample =True,
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repetition_penalty=repetition_penalty,
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)
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return tokenizer.batch_decode(outputs[:,inputs.shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)
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```
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Then:
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```
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messages = [
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{"role": "system", "content": "You are a friendly materials scientist."},
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{"role": "user", "content": "What is the strongest spider silk material?"},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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output_text=generate_response (text_input=prompt, eos_token_id=eos_token,
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num_return_sequences=1, repetition_penalty=1.,
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top_p=0.9, top_k=512,
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temperature=0.1,max_new_tokens=512, verbatim=False,
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)
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print (output_text)
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```
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#### GGUF files: Loading and inference
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```
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from llama_cpp import Llama
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model_path='./BioinspiredZephyr-7B/ggml-model-q5_K_M.gguf'
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chat_format="mistral-instruct"
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llm = Llama(model_path=model_path,
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n_gpu_layers=-1,verbose= True,
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n_ctx=10000,
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#main_gpu=0,
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chat_format=chat_format,
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#split_mode=llama_cpp.LLAMA_SPLIT_LAYER
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)
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```
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Or, download directly from Hugging Face:
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```
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from llama_cpp import Llama
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model_path='lamm-mit/BioinspiredZephyr-7B/ggml-model-q5_K_M.gguf'
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chat_format="mistral-instruct"
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llm = Llama.from_pretrained(
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repo_id=model_path,
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filename="*q5_K_M.gguf",
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verbose=True,
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n_gpu_layers=-1,
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n_ctx=10000,
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#main_gpu=0,
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chat_format=chat_format,
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)
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```
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For inference:
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```
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def generate_BioinspiredZephyr_7B(system_prompt='You are an expert in biological materials, mechanics and related topics.',
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prompt="What is spider silk?",
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temperature=0.0,
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max_tokens=10000,
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):
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if system_prompt==None:
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messages=[
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{"role": "user", "content": prompt},
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]
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else:
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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result=llm.create_chat_completion(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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start_time = time.time()
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result=generate_BioinspiredZephyr_7B(system_prompt='You respond accurately.',
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prompt="What is graphene? Answer with detail.",
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max_tokens=512, temperature=0.7, )
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print (result)
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deltat=time.time() - start_time
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print("--- %s seconds ---" % deltat)
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toked=tokenizer(res)
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print ("Tokens per second (generation): ", len (toked['input_ids'])/deltat)
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```
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arXiv: https://arxiv.org/abs/2309.08788
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config.json
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{
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"_name_or_path": "HuggingFaceH4/zephyr-7b-beta",
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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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"pad_token_id": 2,
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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": "4.37.0.dev0",
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"use_cache": false,
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"vocab_size": 32000
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}
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"_from_model_config": true,
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"transformers_version": "4.37.0.dev0"
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}
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|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00003.safetensors",
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
29
special_tokens_map.json
Normal file
29
special_tokens_map.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "</s>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
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.
49
tokenizer_config.json
Normal file
49
tokenizer_config.json
Normal file
@@ -0,0 +1,49 @@
|
||||
{
|
||||
"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": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"device_map": "auto",
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"truncation_side": "left",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user