137 lines
3.1 KiB
Markdown
137 lines
3.1 KiB
Markdown
---
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license: apache-2.0
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---
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# Mistral-7B-code-16k-qlora
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I'm excited to announce the release of a new model called Mistral-7B-code-16k-qlora. This small and fast model shows a lot of promise for supporting coding or acting as a copilot. I'm currently looking for people to help me test it out!
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## Additional Information
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This model was trained on 3x RTX 3090 in my homelab, using around 65kWh for approximately 23 cents, which is equivalent to around $15 for electricity.
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## Quantised:
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1. https://huggingface.co/TheBloke/Mistral-7B-Code-16K-qlora-GPTQ
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2. https://huggingface.co/TheBloke/Mistral-7B-Code-16K-qlora-AWQ
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3. https://huggingface.co/TheBloke/Mistral-7B-Code-16K-qlora-GGUF
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## Download by qBittorrent:
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#### Torrent file: https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-code-16k-qlora.torrent
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## Dataset:
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nickrosh/Evol-Instruct-Code-80k-v1
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https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1
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## Prompt template: Alpaca
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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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"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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## eval plus
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Human eval plus: https://github.com/evalplus/evalplus
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```
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Nondzu mistral-7b-code
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Base
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{'pass@1': 0.3353658536585366}
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Base + Extra
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{'pass@1': 0.2804878048780488}
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```
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to compare here is original Mistral model tested on the same machine
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```
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Mistral 7b
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Base
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{'pass@1': 0.2926829268292683}
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Base + Extra
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{'pass@1': 0.24390243902439024}
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```
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## Settings:
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```
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base_model: mistralai/Mistral-7B-Instruct-v0.1
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base_model_config: mistralai/Mistral-7B-Instruct-v0.1
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: true
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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: nickrosh/Evol-Instruct-Code-80k-v1
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type: oasst
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./Mistral-7B-Evol-Instruct-16k-test11
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adapter: qlora
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lora_model_dir:
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# 16384 8192 4096 2048
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sequence_len: 16384
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sample_packing: true
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pad_to_sequence_len: true
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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_modules:
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project: mistral-code
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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: 2
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micro_batch_size: 1
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num_epochs: 8
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optimizer: paged_adamw_32bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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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: 10
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eval_steps: 20
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save_steps:
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debug:
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# deepspeed:
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deepspeed: deepspeed/zero2.json
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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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Check my other projects:
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https://github.com/Nondzu/LlamaTor |