--- 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: 50.0 verified: false ---

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

### NOTE: Requantized using WizardLM_evol_instruct_V2_196k for calibration * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF) Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 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 | 50.0| [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 |59.64 | | HellaSwag |82.25 | | MMLU | 61.33 | | TruthfulQA | 48.45 | | Average | 62.92 | ## 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 | A40-48G x 2 | | | |------ | ------ | | epoch | 2.0 | | etrain_loss | 0.5 | | etrain_runtime | 1 day, 10:25:26.77 | | etrain_samples_per_second | 3.194 | | etrain_steps_per_second | 0.025 | | eeval_loss | 0.5146 | | eeval_runtime | 0:00:25.04 | | eeval_samples_per_second | 7.985 | | eeval_steps_per_second | |