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Model: UW-Madison-Lee-Lab/Llama-PRM800K 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: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- generated_from_trainer
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model-index:
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- name: Llama-PRM800K
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Llama-PRM800K
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [PRM800K](https://github.com/openai/prm800k/tree/main).
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## Get rewards
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def get_tokenizer(model_id):
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'left'
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tokenizer.truncation_side = 'left'
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return tokenizer
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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tokenizer = get_tokenizer('UW-Madison-Lee-Lab/Llama-PRM800K')
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model = AutoModelForCausalLM.from_pretrained('UW-Madison-Lee-Lab/Llama-PRM800K')
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candidate_tokens = [12, 10]
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model.to(device)
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question = 'Question: In Python 3, which of the following function convert a string to an int in python?\nA. short(x)\nB. float(x)\nC. integer(x [,base])\nD. double(x)\nE. int(x [,base])\nF. long(x [,base] )\nG. num(x)\nH. str(x)\nI. char(x)\nJ. digit(x [,base])'
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solution = ["To convert a string to an integer in Python 3, we use the built-in function int().",
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"The int() function takes two arguments: the string to be converted and an optional base (default is 10, which is for decimal).",
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"For example: int(\"123\", 10) converts the string \"123\" to the integer 123.",
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"Looking at the options, we can see that the correct function is option E: int(x [,base]).",
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"The answer is (E)."]
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input_text = question + ' \n\n' + ' \n\n\n\n'.join(solution) + ' \n\n\n\n' # solution steps are separated by ' \n\n\n\n'
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input_id = torch.tensor([tokenizer.encode(input_text)]).to(device)
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with torch.no_grad():
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logits = model(input_id).logits[:,:,candidate_tokens]
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scores = logits.softmax(dim=-1)[:,:,1]
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step_scores = scores[input_id == 23535]
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step_probs = step_scores.tolist()
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```
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config.json
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config.json
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{
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"_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 128,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"temperature": 0.6,
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17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
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|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3499d775f2c2601ebb70f05a8a397ab1e33c7dd2c1a7c9d334a8f1f6462ed367
|
||||
size 6520
|
||||
Reference in New Issue
Block a user