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Model: HumanLLMs/Human-Like-LLama3-8B-Instruct Source: Original Platform
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315
README.md
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README.md
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---
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license: llama3
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tags:
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- axolotl
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- dpo
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- trl
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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datasets:
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- HumanLLMs/Human-Like-DPO-Dataset
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model-index:
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- name: Humanish-LLama3.1-8B-Instruct
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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: 64.98
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name: strict accuracy
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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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: 28.01
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name: normalized accuracy
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source:
|
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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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: 8.46
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name: exact match
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source:
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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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: 0.78
|
||||
name: acc_norm
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source:
|
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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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: 2
|
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name: acc_norm
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source:
|
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url: >-
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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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: 30.02
|
||||
name: accuracy
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||||
source:
|
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url: >-
|
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https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
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name: Open LLM Leaderboard
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pipeline_tag: text-generation
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library_name: transformers
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---
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<div align="center">
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<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/63da3d7ae697e5898cb86854/H-vpXOX6KZu01HnV87Jk5.jpeg" width="320" height="320" />
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<h1>Enhancing Human-Like Responses in Large Language Models</h1>
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</div>
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<p align="center">
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   | 🤗 <a href="https://huggingface.co/collections/HumanLLMs/human-like-humanish-llms-6759fa68f22e11eb1a10967e">Models</a>   |
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   📊 <a href="https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset">Dataset</a>   |
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   📄<a href="https://arxiv.org/abs/2501.05032">Paper</a>   |
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</p>
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<p align="center">
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<b>📢 The paper associated with this model has been accepted to the <a href="https://personalizedllm.github.io/events/perfm-aaai26/">AAAI-26 Workshop on Personalization in the Era of Large Foundation Models (PerFM)</a>.</b>
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</p>
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# 🚀 Human-Like-Llama3-8B-Instruct
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), specifically optimized to generate more human-like and conversational responses.
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The fine-tuning process employed both [Low-Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685) and [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions.
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The proccess of creating this models is detailed in the research paper [“Enhancing Human-Like Responses in Large Language Models”](https://arxiv.org/abs/2501.05032).
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# 🛠️ Training Configuration
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- **Base Model:** Llama3-8B-Instruct
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- **Framework:** Axolotl v0.4.1
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- **Hardware:** 2x NVIDIA A100 (80 GB) GPUs
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- **Training Time:** ~2 hours 20 minutes
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- **Dataset:** Synthetic dataset with ≈11,000 samples across 256 diverse topics
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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chat_template: llama3
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rl: dpo
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datasets:
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- path: HumanLLMs/humanish-dpo-project
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type: llama3.prompt_pairs
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chat_template: llama3
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./humanish-llama3-8b-instruct
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sequence_len: 8192
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sample_packing: false
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pad_to_sequence_len: true
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|
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adapter: lora
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lora_model_dir:
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lora_r: 8
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lora_alpha: 4
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project: Humanish-DPO
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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|
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hub_model_id: HumanLLMs/Humanish-LLama3.1-8B-Instruct
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|
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gradient_accumulation_steps: 8
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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|
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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||||
|
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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s2_attention:
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|
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warmup_steps: 10
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evals_per_epoch: 2
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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save_safetensors: true
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```
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</details><br>
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# 💬 Prompt Template
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You can use Llama3 prompt template while using the model:
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### Llama3
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```
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<|start_header_id|>system<|end_header_id|>
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{system}<|eot_id|>
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<|start_header_id|>user<|end_header_id|>
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{user}<|eot_id|>
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<|start_header_id|>assistant<|end_header_id|>
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{assistant}<|eot_id|>
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```
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This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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`tokenizer.apply_chat_template()` method:
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```python
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messages = [
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{"role": "system", "content": "You are helpful AI asistant."},
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{"role": "user", "content": "Hello!"}
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]
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gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
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model.generate(**gen_input)
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```
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# 🤖 Models
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| Model | Download |
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|:---------------------:|:-----------------------------------------------------------------------:|
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| Human-Like-Llama-3-8B-Instruct | 🤗 [HuggingFace](https://huggingface.co/HumanLLMs/Human-Like-LLama3-8B-Instruct) |
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| Human-Like-Qwen-2.5-7B-Instruct | 🤗 [HuggingFace](https://huggingface.co/HumanLLMs/Human-Like-Qwen2.5-7B-Instruct) |
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| Human-Like-Mistral-Nemo-Instruct | 🤗 [HuggingFace](https://huggingface.co/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407) |
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# 🔄 Quantizationed versions
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## GGUF [@bartowski](https://huggingface.co/bartowski)
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- https://huggingface.co/bartowski/Human-Like-LLama3-8B-Instruct-GGUF
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- https://huggingface.co/bartowski/Human-Like-Qwen2.5-7B-Instruct-GGUF
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- https://huggingface.co/bartowski/Human-Like-Mistral-Nemo-Instruct-2407-GGUF
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# 🎯 Benchmark Results
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| **Group** | **Model** | **Average** | **IFEval** | **BBH** | **MATH Lvl 5** | **GPQA** | **MuSR** | **MMLU-PRO** |
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|--------------------------------|--------------------------------|-------------|------------|---------|----------------|----------|----------|--------------|
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| **Llama Models** | Human-Like-Llama-3-8B-Instruct | 22.37 | **64.97** | 28.01 | 8.45 | 0.78 | **2.00** | 30.01 |
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| | Llama-3-8B-Instruct | 23.57 | 74.08 | 28.24 | 8.68 | 1.23 | 1.60 | 29.60 |
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| | *Difference (Human-Like)* | -1.20 | **-9.11** | -0.23 | -0.23 | -0.45 | +0.40 | +0.41 |
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| **Qwen Models** | Human-Like-Qwen-2.5-7B-Instruct | 26.66 | 72.84 | 34.48 | 0.00 | 6.49 | 8.42 | 37.76 |
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| | Qwen-2.5-7B-Instruct | 26.86 | 75.85 | 34.89 | 0.00 | 5.48 | 8.45 | 36.52 |
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| | *Difference (Human-Like)* | -0.20 | -3.01 | -0.41 | 0.00 | **+1.01**| -0.03 | **+1.24** |
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| **Mistral Models** | Human-Like-Mistral-Nemo-Instruct | 22.88 | **54.51** | 32.70 | 7.62 | 5.03 | 9.39 | 28.00 |
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| | Mistral-Nemo-Instruct | 23.53 | 63.80 | 29.68 | 5.89 | 5.37 | 8.48 | 27.97 |
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| | *Difference (Human-Like)* | -0.65 | **-9.29** | **+3.02**| **+1.73** | -0.34 | +0.91 | +0.03 |
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# 📊 Dataset
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The dataset used for fine-tuning was generated using LLaMA 3 models. The dataset includes 10,884 samples across 256 distinct topics such as technology, daily life, science, history, and arts. Each sample consists of:
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- **Human-like responses:** Natural, conversational answers mimicking human dialogue.
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- **Formal responses:** Structured and precise answers with a more formal tone.
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The dataset has been open-sourced and is available at:
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||||
|
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- 👉 [Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset)
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More details on the dataset creation process can be found in the accompanying research paper.
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# 📝 Citation
|
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```
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@misc{çalık2025enhancinghumanlikeresponseslarge,
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title={Enhancing Human-Like Responses in Large Language Models},
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author={Ethem Yağız Çalık and Talha Rüzgar Akkuş},
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year={2025},
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eprint={2501.05032},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2501.05032},
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}
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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/Meta-Llama-3-8B-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": 128009,
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"head_dim": 128,
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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": 8192,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
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"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
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"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.45.1",
|
||||
"use_cache": false,
|
||||
"vocab_size": 128256
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||||
}
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
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12
generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128009
|
||||
],
|
||||
"max_length": 4096,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.45.1"
|
||||
}
|
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3
model-00001-of-00004.safetensors
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3
model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
{
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||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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size 17209961
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2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user