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Model: lunahr/thea-rp-3b-25r Source: Original Platform
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163
README.md
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---
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language:
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- en
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license: llama3.2
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tags:
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- text-generation-inference
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- transformers
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- llama
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- trl
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- sft
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- reasoning
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- llama-3
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base_model: SicariusSicariiStuff/Impish_LLAMA_3B
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datasets:
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- KingNish/reasoning-base-20k
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- lunahr/thea-name-overrides
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model-index:
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- name: thea-rp-3b-25r
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 65.78
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 20.01
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 11.71
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 3.24
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 5.93
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 22.89
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lunahr/thea-rp-3b-25r
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name: Open LLM Leaderboard
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---
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# Model Description
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An uncensored roleplay reasoning Llama 3.2 3B model trained on reasoning data.
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It may potentially be a highest scoring RP finetune of Llama 3.2.
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It has been trained using improved training code, and gives an improved performance.
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Here is what inference code you should use:
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MAX_REASONING_TOKENS = 1024
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MAX_RESPONSE_TOKENS = 512
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model_name = "lunahr/thea-rp-3b-25r"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Which is greater 9.9 or 9.11 ??"
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messages = [
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{"role": "user", "content": prompt}
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]
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# Generate reasoning
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reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
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reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
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reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
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reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("REASONING: " + reasoning_output)
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# Generate answer
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messages.append({"role": "reasoning", "content": reasoning_output})
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response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
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response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
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response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("ANSWER: " + response_output)
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```
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- **Trained by:** [Piotr Zalewski](https://huggingface.co/lunahr)
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- **License:** llama3.2
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- **Finetuned from model:** [SicariusSicariiStuff/Impish_LLAMA_3B](https://huggingface.co/SicariusSicariiStuff/Impish_LLAMA_3B)
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- **Dataset used:** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
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This Llama model was trained faster than [Unsloth](https://github.com/unslothai/unsloth) using [custom training code](https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4).
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Visit https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4 to find out how you can finetune your models using BOTH of the Kaggle provided GPUs.
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36
config.json
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{
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"_name_or_path": "piotr25691/thea-rp-3b-25r",
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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": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.45.2",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.45.2"
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}
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special_tokens_map.json
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17
special_tokens_map.json
Normal file
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3
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Normal file
3
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Normal file
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tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
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