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--- |
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language: |
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- en |
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license: llama2 |
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library_name: transformers |
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tags: |
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- merge |
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- mergekit |
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- lazymergekit |
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datasets: |
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- teknium/openhermes |
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- cognitivecomputations/dolphin |
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base_model: |
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- cognitivecomputations/dolphin-llama2-7b |
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- Tensoic/Llama-2-openhermes |
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pipeline_tag: text-generation |
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model-index: |
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- name: OpenDolphinHermes_Llama2_7B |
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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: 55.03 |
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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=sethuiyer/OpenDolphinHermes_Llama2_7B |
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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: 78.74 |
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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=sethuiyer/OpenDolphinHermes_Llama2_7B |
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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: 52.25 |
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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=sethuiyer/OpenDolphinHermes_Llama2_7B |
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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: 46.1 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B |
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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: 73.16 |
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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=sethuiyer/OpenDolphinHermes_Llama2_7B |
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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: 20.17 |
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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=sethuiyer/OpenDolphinHermes_Llama2_7B |
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name: Open LLM Leaderboard |
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--- |
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# OpenDolphinHermes_Llama2_7B |
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<p align="center"> |
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<img src="https://huggingface.co/sethuiyer/OpenDolphinHermes_Llama2_7B/resolve/main/dolphin_hermes.webp" height="256px" alt="SynthIQ"> |
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</p> |
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|
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mergekit SLERP of these two models |
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* [cognitivecomputations/dolphin-llama2-7b](https://huggingface.co/cognitivecomputations/dolphin-llama2-7b) |
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* [Tensoic/Llama-2-openhermes](https://huggingface.co/Tensoic/Llama-2-openhermes) |
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## 🧩 Configuration |
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```yaml |
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slices: |
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- sources: |
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- model: cognitivecomputations/dolphin-llama2-7b |
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layer_range: [0, 32] |
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- model: Tensoic/Llama-2-openhermes |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: Tensoic/Llama-2-openhermes |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.5, 0.3, 0.7, 1] |
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- filter: mlp |
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value: [1, 0.5, 0.7, 0.3, 0] |
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- value: 0.5 |
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dtype: bfloat16 |
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``` |
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# Prompt Template (ChatML) |
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```text |
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<|im_start|>system |
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You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. |
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Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. |
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Please ensure that your responses are socially unbiased and positive in nature. |
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. |
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If you don't know the answer to a question, please don't share false information. |
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<|im_end|> |
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<|im_start|>user |
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{ .Prompt} |
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<|im_end|> |
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<|im_start|>assistant |
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``` |
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# OpenLLM Leaderboard |
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|
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| T | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
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|---|--------------------------------------------|---------|------|-----------|-------|------------|------------|-------| |
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| 0 | meta-llama/llama-2-13b-hf | 55.69 | 59.39 | 82.13 | 55.77 | 37.38 | 76.64 | 22.82 | |
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| 1 | sethuiyer/OpenDolphinHermes_Llama2_7B | 54.24 | 55.03| 78.74 | 52.25 | 46.1 | 73.16 | 20.17 | |
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| 2 | togethercomputer/Llama-2-7B-32K-Instruct | 50.02 | 51.11| 78.51 | 46.11 | 44.86 | 73.88 | 5.69 | |
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| 3 | togethercomputer/LLaMa-2-7B-32K | 47.07 | 47.53| 76.14 | 43.33 | 39.23 | 71.9 | 4.32 | |
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## Why? |
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I wanted a LLaMa2-7B model which is as good as base LLaMa2-13B model. |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "sethuiyer/OpenDolphinHermes_Llama2_7B" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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Output: |
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```text |
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A large language model is a type of artificial intelligence system that has been trained on a massive amount of data, often millions or even billions of words, to learn the patterns and relationships between words and phrases. |
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These models can then be used to generate new text, understand and translate languages, and perform various natural language processing tasks. |
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They have become increasingly popular in recent years due to advances in machine learning technology and their ability to achieve high levels of accuracy and performance on natural language processing tasks. |
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Examples of large language models include GPT-2, BERT, and T5. |
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``` |
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## Thanks |
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Thanks to Google Colab for the compute. |
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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_sethuiyer__OpenDolphinHermes_Llama2_7B) |
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|
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |54.24| |
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|AI2 Reasoning Challenge (25-Shot)|55.03| |
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|HellaSwag (10-Shot) |78.74| |
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|MMLU (5-Shot) |52.25| |
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|TruthfulQA (0-shot) |46.10| |
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|Winogrande (5-shot) |73.16| |
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|GSM8k (5-shot) |20.17| |
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