---
license: apache-2.0
tags:
- axolotl
- dpo
- trl
- llama-cpp
- gguf-my-repo
base_model: HumanLLMs/Human-Like-Qwen2.5-7B-Instruct
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
language:
- en
model-index:
- name: Humanish-Qwen2.5-7B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 72.84
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 34.48
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.49
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 8.42
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.76
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
      name: Open LLM Leaderboard
---

# Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q4_K_M-GGUF
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)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q4_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Human-Like-Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file human-like-qwen2.5-7b-instruct-q4_k_m.gguf -c 2048
```