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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- nbeerbower/GreatFirewall-DPO |
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- nbeerbower/Schule-DPO |
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- nbeerbower/Purpura-DPO |
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- nbeerbower/Arkhaios-DPO |
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- jondurbin/truthy-dpo-v0.1 |
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- antiven0m/physical-reasoning-dpo |
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- flammenai/Date-DPO-NoAsterisks |
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- flammenai/Prude-Phi3-DPO |
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- Atsunori/HelpSteer2-DPO |
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- jondurbin/gutenberg-dpo-v0.1 |
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- nbeerbower/gutenberg2-dpo |
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- nbeerbower/gutenberg-moderne-dpo |
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base_model: nbeerbower/Dumpling-Qwen2.5-1.5B-v2 |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q4_K_S-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-1.5B-v2) for more details on the model. |
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--- |
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nbeerbower/EVA-abliterated-TIES-Qwen2.5-1.5B finetuned on: |
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nbeerbower/GreatFirewall-DPO |
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nbeerbower/Schule-DPO |
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nbeerbower/Purpura-DPO |
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nbeerbower/Arkhaios-DPO |
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jondurbin/truthy-dpo-v0.1 |
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antiven0m/physical-reasoning-dpo |
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flammenai/Date-DPO-NoAsterisks |
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flammenai/Prude-Phi3-DPO |
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Atsunori/HelpSteer2-DPO (1,000 samples) |
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jondurbin/gutenberg-dpo-v0.1 |
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nbeerbower/gutenberg2-dpo |
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nbeerbower/gutenberg-moderne-dpo. |
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Method |
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QLoRA ORPO tune with 2x RTX 3090 for 2 epochs. |
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# QLoRA config |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch_dtype, |
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bnb_4bit_use_double_quant=True, |
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) |
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# LoRA config |
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peft_config = LoraConfig( |
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r=64, |
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lora_alpha=64, |
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lora_dropout=0.05, |
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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj'] |
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) |
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# Training config |
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orpo_args = ORPOConfig( |
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run_name=new_model, |
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learning_rate=2e-5, |
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lr_scheduler_type="linear", |
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max_length=2048, |
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max_prompt_length=1024, |
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max_completion_length=1024, |
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beta=0.1, |
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per_device_train_batch_size=1, |
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per_device_eval_batch_size=1, |
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gradient_accumulation_steps=8, |
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optim="paged_adamw_8bit", |
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num_train_epochs=2, |
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evaluation_strategy="steps", |
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eval_steps=0.2, |
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logging_steps=1, |
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warmup_steps=10, |
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max_grad_norm=10, |
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report_to="wandb", |
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output_dir="./results/", |
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bf16=True, |
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) |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q4_K_S-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q4_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q4_K_S-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q4_k_s.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q4_K_S-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q4_k_s.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-1.5B-v2-Q4_K_S-GGUF --hf-file dumpling-qwen2.5-1.5b-v2-q4_k_s.gguf -c 2048 |
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``` |
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