---
library_name: transformers
license: apache-2.0
datasets:
- nbeerbower/GreatFirewall-DPO
- nbeerbower/Schule-DPO
- nbeerbower/Purpura-DPO
- nbeerbower/Arkhaios-DPO
- jondurbin/truthy-dpo-v0.1
- antiven0m/physical-reasoning-dpo
- flammenai/Date-DPO-NoAsterisks
- flammenai/Prude-Phi3-DPO
- Atsunori/HelpSteer2-DPO
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
base_model: nbeerbower/Dumpling-Qwen2.5-1.5B-v2
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q5_K_M-GGUF
This model was converted to GGUF format from [`nbeerbower/Dumpling-Qwen2.5-1.5B-v2`](https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-1.5B-v2) 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/nbeerbower/Dumpling-Qwen2.5-1.5B-v2) for more details on the model.

---
nbeerbower/EVA-abliterated-TIES-Qwen2.5-1.5B finetuned on:

    nbeerbower/GreatFirewall-DPO
    nbeerbower/Schule-DPO
    nbeerbower/Purpura-DPO
    nbeerbower/Arkhaios-DPO
    jondurbin/truthy-dpo-v0.1
    antiven0m/physical-reasoning-dpo
    flammenai/Date-DPO-NoAsterisks
    flammenai/Prude-Phi3-DPO
    Atsunori/HelpSteer2-DPO (1,000 samples)
    jondurbin/gutenberg-dpo-v0.1
    nbeerbower/gutenberg2-dpo
    nbeerbower/gutenberg-moderne-dpo.

Method

QLoRA ORPO tune with 2x RTX 3090 for 2 epochs.

# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch_dtype,
    bnb_4bit_use_double_quant=True,
)

# LoRA config
peft_config = LoraConfig(
    r=64,
    lora_alpha=64,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
)

# Training config
orpo_args = ORPOConfig(
    run_name=new_model,
    learning_rate=2e-5,
    lr_scheduler_type="linear",
    max_length=2048,
    max_prompt_length=1024,
    max_completion_length=1024,
    beta=0.1,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    gradient_accumulation_steps=8,
    optim="paged_adamw_8bit",
    num_train_epochs=2,
    evaluation_strategy="steps",
    eval_steps=0.2,
    logging_steps=1,
    warmup_steps=10,
    max_grad_norm=10,
    report_to="wandb",
    output_dir="./results/",
    bf16=True,
)

---
## 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/Dumpling-Qwen2.5-1.5B-v2-Q5_K_M-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q5_K_M-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q5_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/Dumpling-Qwen2.5-1.5B-v2-Q5_K_M-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q5_K_M-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q5_k_m.gguf -c 2048
```