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Model: aloobun/Cypher-Mini-1.8B Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: apache-2.0
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datasets:
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- Locutusque/Hercules-v3.0
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tags:
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- finetune
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- gpt4
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- synthetic data
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- custom_code
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- h2oai
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---
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- This is an experimental model, Finetuned [h2oai/h2o-danube-1.8b-chat](https://huggingface.co/h2oai/h2o-danube-1.8b-chat), on Hercules v3 & private dataset.
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- The original idea was to use this 1.8B model, divide the dataset based on task specific capabilities, train models and transform them into a mixture of experts.
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- Hyperparameters: adamw with eps of 1e-8, cosine decay w/ 20% warmup, lr=2e-5.
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## Format:
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```
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<|system|></s><|prompt|></s><|answer|>
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```
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## Benchamrks:
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WIP
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## Example:
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
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import torch
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class MyStoppingCriteria(StoppingCriteria):
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def __init__(self, target_sequence, prompt):
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self.target_sequence = target_sequence
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self.prompt=prompt
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def __call__(self, input_ids, scores, **kwargs):
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generated_text = tokenizer.decode(input_ids[0])
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generated_text = generated_text.replace(self.prompt,'')
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if self.target_sequence in generated_text:
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return True
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return False
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def __len__(self):
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return 1
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def __iter__(self):
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yield self
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modelpath="aloobun/Cypher-Mini-1.8B"
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model = AutoModelForCausalLM.from_pretrained(
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modelpath,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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modelpath,
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trust_remote_code=True,
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use_fast=False,
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)
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prompt = "<|prompt|>Reflect on a time when you encountered a logical fallacy in an argument. How did you identify it, and what was the consequence?</s><|answer|>"
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encoded_input = tokenizer(prompt, return_tensors='pt')
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input_ids=encoded_input['input_ids'].cuda()
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streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
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op = model.generate(
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input_ids,
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streamer=streamer,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.7,
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top_p=0.8,
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max_new_tokens=512,
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stopping_criteria=MyStoppingCriteria("</s>", prompt)
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)
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```
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## Output:
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>I do not have personal experiences or emotions, but I can provide you with an example of a logical fallacy and its consequences:
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>
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>One common logical fallacy is the appeal to authority fallacy. This occurs when someone argues that a particular opinion or belief is true because of who holds it (i.e., "because the doctor said so"). However, this approach does not take into account other factors that may influence the validity of the claim. For instance, if a doctor says that eating a certain food will cure cancer, it does not necessarily mean that it will work for everyone. Other factors such as genetics, lifestyle, and environmental factors could also play a role in whether or not a person gets cancer.
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>
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>The consequence of using the appeal to authority fallacy is that it often leads to hasty conclusions and misinformation. It can be difficult to separate fact from fiction, especially when people rely on authority figures to make decisions. As a result, individuals may end up making poor choices based on incomplete information. This can lead to unintended consequences, such as harming oneself or others.
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>
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>To avoid falling prey to the appeal to authority fallacy, it is important to seek out multiple sources of information and consider all available evidence before making a decision. This can help individuals make more informed choices and reduce the likelihood of being swayed by unsubstantiated claims.</s>
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config.json
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config.json
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{
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"_name_or_path": "h2oai/h2o-danube-1.8b-chat",
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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": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"max_position_embeddings": 16384,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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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",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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"_from_model_config": true,
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"pad_token_id": 0,
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"repetition_penalty": 1.1,
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"transformers_version": "4.37.0"
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}
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||||
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|
||||
}
|
||||
}
|
||||
44
special_tokens_map.json
Normal file
44
special_tokens_map.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"cls_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": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sep_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93405
tokenizer.json
Normal file
93405
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.
45
tokenizer_config.json
Normal file
45
tokenizer_config.json
Normal file
@@ -0,0 +1,45 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": 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
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|prompt|>' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ '<|system|>' + message['content'] + eos_token }}{% elif message['role'] == 'assistant' %}{{ '<|answer|>' + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|answer|>' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"cls_token": "</s>",
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<unk>",
|
||||
"padding_side": "left",
|
||||
"sep_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user