--- language: - en library_name: transformers pipeline_tag: text-generation datasets: - jondurbin/airoboros-2.2 - Open-Orca/OpenOrca - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_V2_196k - TokenBender/python_eval_instruct_51k tags: - llama-2 - code license: llama2 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 51.829 verified: false ---

speechless-tora-code-7b-v1.0

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-tora-code-7B-v1.0-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-tora-code-7B-v1.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-tora-code-7B-v1.0-GGUF) Code: https://github.com/uukuguy/speechless Use the following dataset to fine-tune llm_agents/tora-code-7b-v1.0 in order to improve the model's reasoning and planning abilities. Total 201,981 samples. - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples ## HumanEval | Metric | Value | | --- | --- | | humaneval-python | 51.829 | [Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) CodeLlama-34B-Python: 53.29 CodeLlama-34B-Instruct: 50.79 CodeLlama-13B-Instruct: 50.6 CodeLlama-34B: 45.11 CodeLlama-13B-Python: 42.89 CodeLlama-13B: 35.07 ## LM-Evaluation-Harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | Metric | Value | | --- | --- | | ARC | 42.66 | | HellaSwag | 65.16 | | MMLU | 38.56 | | TruthfulQA | 42.06 | | Average | 47.11 | ## Parameters | | | |------ | ------ | | lr | 2e-4 | | lr_scheduler_type | cosine | | weight_decay | 0.0 | | optim | paged_adamw_8bit | | flash_attention | True | | rerope | False | | max_new_tokens | 4096 | | num_train_epochs | 2 | | bits | 4 | | lora_r | 64 | | lora_alpha | 16 | | lora_dropout | 0.05 | | double_quant | True | | quant_type | nf4 | | dataset_format | airoboros | | mini_batch_size | 2 | | grandient_accumulation_steps | 32 | | bf16 | True | A800-80G x 2 | | | |------ | ------ | | epoch | 2.0 | | etrain_loss | 0.5891 | | etrain_runtime | 19:24:49.43 | | etrain_samples_per_second | 5.664 | | etrain_steps_per_second | 0.044 | | eeval_loss | 0.5872 | | eeval_runtime | 0:00:15.59 | | eeval_samples_per_second | 12.822 | | eeval_steps_per_second | 6.411 |