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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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--- |
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# 🥷 Safurai-Csharp-34B |
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📝 [Article](https://www.safurai.com/blog/introducing-safurai-csharp) |
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📄 [Paper](https://www.safurai.com/) |
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<center><img src="https://i.imgur.com/REPqbYM.png" width="300"></center> |
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This is a [`codellama/CodeLlama-34b-hf`](https://huggingface.co/codellama/CodeLlama-34b-hf) model fine-tuned using QLoRA (4-bit precision) on 13B tokens of csharp evolved Q&A |
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We obtained <b>state-of-the-art performance</b> on the MultiPL-E code LLM benchmark for csharp, reaching 56% at pass@1 with n=5. |
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## 💻 Quantization |
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These are GGUF quantized versions of Safurai-Csharp-34B, it has been made by using the amazing [`llama.cpp`](https://github.com/ggerganov/llama.cpp) library. |
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## 🔧 Training |
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It was trained on 2 x NVIDIA A100 PCIe 80GB in 7h 40m with the following configuration file: |
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```yaml |
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base_model: codellama/CodeLlama-34b-hf |
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base_model_config: codellama/CodeLlama-34b-hf |
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model_type: LlamaForCausalLM |
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tokenizer_type: CodeLlamaTokenizer |
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is_llama_derived_model: true |
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hub_model_id: "Safurai/Evol-csharp-v1" |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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datasets: |
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- path: Safurai/EvolInstruct-csharp-16k-13B-Alpaca |
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type: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.01 |
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output_dir: ./qlora-out |
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sequence_len: 4096 |
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sample_packing: true |
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pad_to_sequence_len: true |
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adapter: lora |
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lora_model_dir: |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: codellama-csharp |
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wandb_entity: |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0003 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 40 |
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eval_steps: 40 |
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save_steps: |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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## 📉 Training loss curve: |
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<img src="https://i.imgur.com/rp1htuf.png" width="500"> |
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## 📊 Dataset composition: |
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<img src="https://i.imgur.com/kTNXgGX.png" width="500"> |
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## 💻 Usage for GGUF |
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``` python |
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# disclaimer: you have to use the llama.gguf library to run this code |
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import os |
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model_list = [file for file in os.listdir(MODEL_NAME) if GGML_VERSION in file] |
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prompt = input("Enter your prompt: ") |
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chosen_method = input("Please specify the quantization method to run the model (options: " + ", ".join(model_list) + "): ") |
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# Verify the chosen method is in the list |
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if chosen_method not in model_list: |
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print("Invalid method chosen!") |
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else: |
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qtype = f"{MODEL_NAME}/{MODEL_NAME.lower()}.{GGML_VERSION}.{method}.bin" |
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!./llama.cpp/main -m {qtype} -n 128 --color -ngl 35 -p "{prompt}" |
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``` |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
