Update README.md

README.md

@@ -112,6 +112,125 @@ model-index:
 This model was converted to GGUF format from [`HumanLLMs/Human-Like-Qwen2.5-7B-Instruct`](https://huggingface.co/HumanLLMs/Human-Like-Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/HumanLLMs/Human-Like-Qwen2.5-7B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct, specifically optimized to generate more human-like and conversational responses.
+
+The fine-tuning process employed both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions.
+
+The proccess of creating this models is detailed in the research paper “Enhancing Human-Like Responses in Large Language Models”.
+🛠️ Training Configuration
+
+    Base Model: Qwen2.5-7B-Instruct
+    Framework: Axolotl v0.4.1
+    Hardware: 2x NVIDIA A100 (80 GB) GPUs
+    Training Time: ~2 hours 15 minutes
+    Dataset: Synthetic dataset with ≈11,000 samples across 256 diverse topics
+
+See axolotl config
+
+axolotl version: 0.4.1
+
+base_model: Qwen/Qwen2.5-7B-Instruct
+model_type: AutoModalForCausalLM
+tokenizer_type: AutoTokenizer
+
+trust_remote_code: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+chat_template: chatml
+rl: dpo
+datasets:
+  - path: HumanLLMs/humanish-dpo-project
+    type: chatml.prompt_pairs
+    chat_template: chatml
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./humanish-qwen2.5-7b-instruct
+
+sequence_len: 8192
+sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 8
+lora_alpha: 4
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project: Humanish-DPO
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+hub_model_id: HumanLLMs/Humanish-Qwen2.5-7B-Instruct
+
+gradient_accumulation_steps: 8
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 2
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+
+save_safetensors: true
+
+
+💬 Prompt Template
+
+You can use ChatML prompt template while using the model:
+ChatML
+
+<|im_start|>system
+{system}<|im_end|>
+<|im_start|>user
+{user}<|im_end|>
+<|im_start|>assistant
+{asistant}<|im_end|>
+
+This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:
+
+messages = [
+    {"role": "system", "content": "You are helpful AI asistant."},
+    {"role": "user", "content": "Hello!"}
+]
+gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
+model.generate(**gen_input)
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)