--- language: - en license: apache-2.0 tags: - text-generation - large-language-model - orpo base_model: - mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: Coven 7B 128K ORPO description: "Coven 7B 128K ORPO is a derivative of Mistral-7B-Instruct-v0.2, fine-tuned to perform specialized tasks involving deeper understanding and reasoning over context. This model exhibits strong capabilities in both general language understanding and task-specific challenges." results: - task: type: text-generation name: Winogrande Challenge dataset: name: Winogrande type: winogrande_xl split: test args: num_few_shot: 5 metrics: - type: accuracy value: 77.82 name: accuracy - task: type: text-generation name: TruthfulQA Generation dataset: name: TruthfulQA type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: accuracy value: 49.55 name: accuracy - task: type: text-generation name: PIQA Problem Solving dataset: name: PIQA type: piqa split: validation args: num_few_shot: 5 metrics: - type: accuracy value: 82.05 name: accuracy - task: type: text-generation name: OpenBookQA Facts dataset: name: OpenBookQA type: openbookqa split: test args: num_few_shot: 5 metrics: - type: accuracy value: 34.60 name: accuracy - task: type: text-generation name: MMLU Knowledge Test dataset: name: MMLU type: mmlu config: all split: test args: num_few_shot: 5 metrics: - type: accuracy value: 63.00 name: accuracy - task: type: text-generation name: Hellaswag Contextual Completions dataset: name: Hellaswag type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: accuracy value: 65.37 name: accuracy - task: type: text-generation name: GSM8k Mathematical Reasoning dataset: name: GSM8k type: gsm8k split: test args: num_few_shot: 5 metrics: - type: accuracy value: 72.18 name: exact match (strict) - type: accuracy value: 72.63 name: exact match (flexible) - task: type: text-generation name: BoolQ Question Answering dataset: name: BoolQ type: boolq split: validation args: num_few_shot: 5 metrics: - type: accuracy value: 87.43 name: accuracy - task: type: text-generation name: ARC Challenge dataset: name: ARC Challenge type: ai2_arc split: test args: num_few_shot: 25 metrics: - type: accuracy value: 59.64 name: accuracy --- # 🧙 Coven 7B 128K ORPO Coven 7B 128K is an improved iteration of Mistral-7B-Instruct-v0.2, refined to expand processing capabilities and refine language model preferences. This model includes a significantly increased context constraint of 128K tokens using the [Yarn](https://github.com/jquesnelle/yarn) technique, which allows for more extensive data processing and understanding of complex language scenarios. In addition, the Coven 7B ORPO 128K tokenization uses the innovative ORPO (Monolithic Preference Optimization without Reference Model) technology. ORPO simplifies the fine-tuning process by directly optimizing the odds ratio to distinguish between favorable and unfavorable generation styles, effectively improving model performance without the need for an additional preference alignment step. ### Eval | Task | Model | Metric | Value | Change (%) | |---------------------|-------------------------|-------------------|----------|------------------------------| | Winogrande | Mistral-7B-Instruct-v0.2| Accuracy | 73.64% | - | | | Coven 7B 128K ORPO | Accuracy | 77.82% | +5.67% | | TruthfulQA | Mistral-7B-Instruct-v0.2| Accuracy | 59.54% | - | | | Coven 7B 128K ORPO | Accuracy | 49.55% | -16.78% | | PIQA | Mistral-7B-Instruct-v0.2| Accuracy | 80.03% | - | | | Coven 7B 128K ORPO | Accuracy | 82.05% | +2.52% | | OpenBookQA | Mistral-7B-Instruct-v0.2| Accuracy | 36.00% | - | | | Coven 7B 128K ORPO | Accuracy | 34.60% | -3.89% | | | Mistral-7B-Instruct-v0.2| Accuracy Normalized| 45.20% | - | | | Coven 7B 128K ORPO | Accuracy Normalized| 48.00% | +6.19% | | MMLU | Mistral-7B-Instruct-v0.2| Accuracy | 58.79% | - | | | Coven 7B 128K ORPO | Accuracy | 63.00% | +7.16% | | Hellaswag | Mistral-7B-Instruct-v0.2| Accuracy | 66.08% | - | | | Coven 7B 128K ORPO | Accuracy | 65.37% | -1.08% | | | Mistral-7B-Instruct-v0.2| Accuracy Normalized| 83.68% | - | | | Coven 7B 128K ORPO | Accuracy Normalized| 84.29% | +0.73% | | GSM8K (Strict) | Mistral-7B-Instruct-v0.2| Exact Match | 41.55% | - | | | Coven 7B 128K ORPO | Exact Match | 72.18% | +73.65% | | GSM8K (Flexible) | Mistral-7B-Instruct-v0.2| Exact Match | 41.93% | - | | | Coven 7B 128K ORPO | Exact Match | 72.63% | +73.29% | | BoolQ | Mistral-7B-Instruct-v0.2| Accuracy | 85.29% | - | | | Coven 7B 128K ORPO | Accuracy | 87.43% | +2.51% | | ARC Easy | Mistral-7B-Instruct-v0.2| Accuracy | 81.36% | - | | | Coven 7B 128K ORPO | Accuracy | 85.02% | +4.50% | | | Mistral-7B-Instruct-v0.2| Accuracy Normalized| 76.60% | - | | | Coven 7B 128K ORPO | Accuracy Normalized| 82.95% | +8.29% | | ARC Challenge | Mistral-7B-Instruct-v0.2| Accuracy | 54.35% | - | | | Coven 7B 128K ORPO | Accuracy | 59.64% | +9.74% | | | Mistral-7B-Instruct-v0.2| Accuracy Normalized| 55.80% | - | | | Coven 7B 128K ORPO | Accuracy Normalized| 61.69% | +10.52% | ## Model Details * **Model name**: Coven 7B 128K ORPO alpha * **Fine-tuned by**: raidhon * **Base model**: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) * **Parameters**: 7B * **Context**: 128K * **Language(s)**: Multilingual * **License**: Apache2.0 ## 💻 Usage ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="raidhon/coven_7b_128k_orpo_alpha", torch_dtype=torch.float16, device_map="auto") messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=4096, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```