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--- |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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datasets: |
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- jondurbin/airoboros-2.2 |
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- Open-Orca/OpenOrca |
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- garage-bAInd/Open-Platypus |
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- WizardLM/WizardLM_evol_instruct_V2_196k |
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- TokenBender/python_eval_instruct_51k |
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- codefuse-ai/Evol-Instruction-66k |
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tags: |
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- llama-2 |
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- code |
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license: llama2 |
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model-index: |
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- name: SpeechlessCoder |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: openai_humaneval |
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name: HumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: |
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verified: false |
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--- |
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<p><h1> speechless-sparsetral-16x7b-MoE </h1></p> |
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speechless-sparsetral-16x7b-MoE is the MoE upgraded version of [speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0). The MoE fine-tuning adopts [Parameter-Efficient Sparsity Crafting (PESC)](https://arxiv.org/abs/2401.02731), which is an efficient fine-tuning architecture that uses LoRA modules as expert models, similar to the concept of [multi-loras](https://github.com/uukuguy/multi_loras). The model size is approximately **10B**. |
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Specifically, Mistral-7B-0.1 is used as the base model, with 16 experts and 4 expert outputs selected for inference. The fine-tuning dataset includes codefuse-ai/Evol-Instruction-66k to enhance the model's code generation ability. The specific datasets are as follows: |
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- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. |
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- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. |
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- garage-bAInd/Open-Platypus: 100%, 24,926 samples. |
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- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples |
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- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples |
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- Spider: 8,659 samples |
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- codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples |
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## Alpaca Prompt Format |
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``` |
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### Instruction: |
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<instruction> |
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### Response: |
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``` |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name_or_path="uukuguy/speechless-sparsetral-16x7b-MoE" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True).eval() |
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system = ""Below is an instruction that describes a task.\nWrite a response that appropriately completes the request.\n\n"" |
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prompt = f"{system}\n\n### Instruction:\n{instruction}\n\n### Response:" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1) |
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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``` |
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