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--- |
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license: apache-2.0 |
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library_name: transformers |
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model-index: |
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- name: laser-dolphin-mixtral-2x7b-dpo |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 65.96 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 85.8 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 63.17 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 60.76 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 79.01 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 48.29 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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name: Open LLM Leaderboard |
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--- |
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# Laser-Dolphin-Mixtral-2x7b-dpo |
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**New Version out now!** |
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Credit to Fernando Fernandes and Eric Hartford for their project [laserRMT](https://github.com/cognitivecomputations/laserRMT) |
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## Overview |
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This model is a medium-sized MoE implementation based on [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) |
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+ The new version shows ~1 point increase in evaluation performance on average. |
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## Process |
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+ The process is outlined in this [notebook](https://github.com/cognitivecomputations/laserRMT/blob/main/examples/laser-dolphin-mixtral-2x7b.ipynb) |
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+ The mergekit_config is in the files. |
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+ The models used in the configuration are not lasered, but the final product is. This is an update from the last version. |
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+ This process is experimental. Your mileage may vary. |
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## Future Goals |
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+ [ ] Function Calling |
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+ [ ] v2 with new base model to improve performance |
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## Quantizations |
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### ExLlamav2 |
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_These are the recommended quantizations for users that are running the model on GPU_ |
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Thanks to user [bartowski](https://huggingface.co/bartowski) we now have exllamav2 quantizations in 3.5 through 8 bpw. They are available here: |
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+ [bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2) |
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| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | |
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| ----- | ---- | ------- | ------ | ------ | ------ | ------------ | |
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| [8_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/8_0) | 8.0 | 8.0 | 13.7 GB | 15.1 GB | 17.2 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | |
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| [6_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/6_5) | 6.5 | 8.0 | 11.5 GB | 12.9 GB | 15.0 GB | Near unquantized performance at vastly reduced size, **recommended**. | |
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| [5_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/5_0) | 5.0 | 6.0 | 9.3 GB | 10.7 GB | 12.8 GB | Slightly lower quality vs 6.5, great for 12gb cards with 16k context. | |
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| [4_25](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/4_25) | 4.25 | 6.0 | 8.2 GB | 9.6 GB | 11.7 GB | GPTQ equivalent bits per weight. | |
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| [3_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/3_5) | 3.5 | 6.0 | 7.0 GB | 8.4 GB | 10.5 GB | Lower quality, not recommended. | |
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His quantizations represent the first ~13B model with GQA support. Check out his repo for more information! |
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### GGUF |
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*Current GGUF [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF)* |
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### AWQ |
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*Current AWQ [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-AWQ) |
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### TheBloke |
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**These Quants will result in unpredicted behavior. New quants are available as I have updated the model** |
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Quatizations provided by [TheBloke](https://huggingface.co/TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF) |
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## HF Spaces |
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+ GGUF chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat-GGUF) |
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+ 4-bit bnb chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat) |
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# Ollama |
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```bash |
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ollama run macadeliccc/laser-dolphin-mixtral-2x7b-dpo |
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``` |
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## Code Example |
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Switch the commented model definition to use in 4-bit. Should work with 9GB and still exceed the single 7B model by 5-6 points roughly |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def generate_response(prompt): |
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""" |
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Generate a response from the model based on the input prompt. |
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Args: |
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prompt (str): Prompt for the model. |
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Returns: |
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str: The generated response from the model. |
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""" |
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# Tokenize the input prompt |
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inputs = tokenizer(prompt, return_tensors="pt") |
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# Generate output tokens |
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outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) |
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# Decode the generated tokens to a string |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return response |
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# Load the model and tokenizer |
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model_id = "macadeliccc/laser-dolphin-mixtral-2x7b-dpo" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) |
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prompt = "Write a quicksort algorithm in python" |
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# Generate and print responses for each language |
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print("Response:") |
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print(generate_response(prompt), "\n") |
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``` |
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[colab](https://colab.research.google.com/drive/1cmRhAkDWItV7utHNqNANVZnqDqQNsTUr?usp=sharing) with usage example |
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## Eval |
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## EQ Bench |
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<pre>----Benchmark Complete---- |
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2024-01-31 16:55:37 |
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Time taken: 31.1 mins |
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Prompt Format: ChatML |
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Model: macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF |
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Score (v2): 72.76 |
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Parseable: 171.0 |
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--------------- |
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Batch completed |
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Time taken: 31.2 mins |
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--------------- |
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</pre> |
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evaluation [colab](https://colab.research.google.com/drive/1FpwgsGzCR4tORTxAwUxpN3PcP22En2xk?usp=sharing) |
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## Summary of previous evaluation |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|---------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)| 41.31| 73.67| 61.69| 42.79| 54.87| |
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## Detailed current evaluation |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|---------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)| 42.25| 73.45| 63.44| 43.96| 55.77| |
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### AGIEval |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------|------:|--------|----:|---|-----:| |
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|agieval_aqua_rat | 0|acc |21.26|± | 2.57| |
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| | |acc_norm|21.65|± | 2.59| |
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|agieval_logiqa_en | 0|acc |34.72|± | 1.87| |
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| | |acc_norm|35.64|± | 1.88| |
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|agieval_lsat_ar | 0|acc |26.96|± | 2.93| |
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| | |acc_norm|26.96|± | 2.93| |
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|agieval_lsat_lr | 0|acc |45.88|± | 2.21| |
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| | |acc_norm|46.08|± | 2.21| |
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|agieval_lsat_rc | 0|acc |59.48|± | 3.00| |
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| | |acc_norm|59.48|± | 3.00| |
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|agieval_sat_en | 0|acc |73.79|± | 3.07| |
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| | |acc_norm|73.79|± | 3.07| |
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|agieval_sat_en_without_passage| 0|acc |42.23|± | 3.45| |
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| | |acc_norm|41.26|± | 3.44| |
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|agieval_sat_math | 0|acc |37.27|± | 3.27| |
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| | |acc_norm|33.18|± | 3.18| |
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Average: 42.25% |
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### GPT4All |
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| Task |Version| Metric |Value| |Stderr| |
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|-------------|------:|--------|----:|---|-----:| |
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|arc_challenge| 0|acc |58.36|± | 1.44| |
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| | |acc_norm|58.02|± | 1.44| |
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|arc_easy | 0|acc |82.20|± | 0.78| |
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| | |acc_norm|77.40|± | 0.86| |
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|boolq | 1|acc |87.52|± | 0.58| |
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|hellaswag | 0|acc |67.50|± | 0.47| |
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| | |acc_norm|84.43|± | 0.36| |
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|openbookqa | 0|acc |34.40|± | 2.13| |
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| | |acc_norm|47.00|± | 2.23| |
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|piqa | 0|acc |81.61|± | 0.90| |
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| | |acc_norm|82.59|± | 0.88| |
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|winogrande | 0|acc |77.19|± | 1.18| |
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Average: 73.45% |
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### GSM8K |
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|Task |Version| Metric |Value| |Stderr| |
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|-----|------:|-----------------------------|-----|---|------| |
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|gsm8k| 2|exact_match,get-answer | 0.75| | | |
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| | |exact_match_stderr,get-answer| 0.01| | | |
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| | |alias |gsm8k| | | |
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### TruthfulQA |
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| Task |Version|Metric|Value| |Stderr| |
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|-------------|------:|------|----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |45.90|± | 1.74| |
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| | |mc2 |63.44|± | 1.56| |
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Average: 63.44% |
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### Bigbench |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------------------------|------:|---------------------|----:|---|-----:| |
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|bigbench_causal_judgement | 0|multiple_choice_grade|58.42|± | 3.59| |
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|bigbench_date_understanding | 0|multiple_choice_grade|60.70|± | 2.55| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|38.37|± | 3.03| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|21.73|± | 2.18| |
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| | |exact_str_match | 0.00|± | 0.00| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|35.00|± | 2.14| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|23.57|± | 1.61| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|50.33|± | 2.89| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|45.00|± | 2.23| |
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|bigbench_navigate | 0|multiple_choice_grade|50.00|± | 1.58| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|60.35|± | 1.09| |
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|bigbench_ruin_names | 0|multiple_choice_grade|51.12|± | 2.36| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|32.26|± | 1.48| |
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|bigbench_snarks | 0|multiple_choice_grade|67.96|± | 3.48| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|70.59|± | 1.45| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|35.80|± | 1.52| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.56|± | 1.18| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|17.20|± | 0.90| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|50.33|± | 2.89| |
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Average: 43.96% |
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Average score: 55.77% |
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Elapsed time: 02:43:45 |
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## Citations |
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Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024. |
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```bibtex |
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@article{sharma2023truth, |
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title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction}, |
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author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra}, |
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journal={arXiv preprint arXiv:2312.13558}, |
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year={2023} } |
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``` |
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```bibtex |
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@article{gao2021framework, |
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title={A framework for few-shot language model evaluation}, |
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author={Gao, Leo and Tow, Jonathan and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and McDonell, Kyle and Muennighoff, Niklas and others}, |
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journal={Version v0. 0.1. Sept}, |
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year={2021} |
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} |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |67.16| |
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|AI2 Reasoning Challenge (25-Shot)|65.96| |
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|HellaSwag (10-Shot) |85.80| |
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|MMLU (5-Shot) |63.17| |
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|TruthfulQA (0-shot) |60.76| |
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|Winogrande (5-shot) |79.01| |
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|GSM8k (5-shot) |48.29| |
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