Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q5_K_M-GGUF

This model was converted to GGUF format from HumanLLMs/Human-Like-Qwen2.5-7B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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)

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q5_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q5_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q5_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q5_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q5_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q5_k_m.gguf -c 2048