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Model: meraGPT/mera-mix-4x7B Source: Original Platform
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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.ipynb_checkpoints/added_tokens-checkpoint.json
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.ipynb_checkpoints/added_tokens-checkpoint.json
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{
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"<sep>": 32002,
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"<|end_of_turn|>": 32000,
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"<|pad_0|>": 32001
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}
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.ipynb_checkpoints/config-checkpoint.json
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.ipynb_checkpoints/config-checkpoint.json
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{
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"_name_or_path": "berkeley-nest/Starling-LM-7B-alpha",
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"architectures": [
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"MixtralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 1,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_local_experts": 4,
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"output_router_logits": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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||||
"transformers_version": "4.39.3",
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"use_cache": true,
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"vocab_size": 32002
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}
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71
.ipynb_checkpoints/mergekit_moe_config-checkpoint.yml
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.ipynb_checkpoints/mergekit_moe_config-checkpoint.yml
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base_model: chihoonlee10/T3Q-EN-DPO-Mistral-7B
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gate_mode: cheap_embed # one of "hidden", "cheap_embed", or "random"
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dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
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## (optional)
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experts_per_token: 2
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experts:
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- source_model: Kukedlc/Jupiter-k-7B-slerp
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positive_prompts:
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- "This puzzle involves identifying a repeating pattern. Can you analyze the examples and complete the missing element?"
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- "These grids seem to follow a specific visual pattern. Can you analyze the examples and apply the pattern to solve the puzzle?"
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- "Look for a consistent rule in how the elements are arranged. Can you use that rule to predict the missing element?"
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- "This task requires applying logical deduction. Can you analyze the information and choose the answer that follows logically?"
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- "Reason through the cause-and-effect relationships in the examples. Can you use that logic to solve the puzzle?"
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- "Analyze the relationship between the input and output in the examples. Can you apply that relationship to solve the new problem?"
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- "This puzzle requires modifying the image based on a specific rule. Can you analyze the changes in the examples and apply them to solve the puzzle?"
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- "Focus on the visual changes demonstrated in the training examples. Can you replicate those changes to solve the new problem?"
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- "This task involves manipulating shapes or colors based on a pattern. Can you analyze the examples and apply the pattern to the new image?"
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- "These puzzles involve manipulating numbers according to a specific rule. Can you analyze the pattern and solve the missing number?"
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- "Focus on the mathematical operations demonstrated in the examples. Can you apply those operations to solve the new equation?"
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- "Look for relationships between the numbers in the training examples. Can you use that relationship to predict the missing number?"
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- "This task requires understanding the arrangement of objects in space. Can you analyze the movement patterns in the examples and predict the next step?"
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- "Focus on the spatial relationships between elements in the grids. Can you replicate those relationships to solve the new puzzle?"
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- "Analyze the rotation, reflection, or translation demonstrated in the examples. Can you apply that manipulation to solve the new problem?"
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- source_model: InferenceIllusionist/Excalibur-7b-DPO
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positive_prompts:
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- "This passage contains factual information. Can you summarize the key details about [topic]?"
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- "Based on the information provided, what can you tell me about [entity]?"
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- "Is the following statement true or false according to the passage: [statement]?"
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- "What caused [event] to happen in the passage?"
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- "Why did [character] take the action of [action]?"
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- "If [condition] were true, what would likely happen next?"
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- "How are [entity A] and [entity B] similar/different?"
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- "Which option, [A] or [B], is more likely based on the information provided?"
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- "Rank the following options ([list]) based on [criteria] according to the passage."
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- "Does the following statement logically follow from the information provided: [statement]?"
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- "Identify any inconsistencies or contradictions in the passage."
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- "Can you draw a logical conclusion based on the evidence presented?"
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- "What can be inferred about [concept] based on the information provided?"
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- "What is the underlying meaning or implication of the author's statement?"
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- "Can you fill in the blanks with the most likely word(s) based on the context?"
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- "What are some potential consequences of [event]?"
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- "Can you generate creative solutions to the problem presented in the passage?"
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- "Based on the information provided, propose a course of action for [character]."
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- source_model: yam-peleg/Experiment21-7B
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positive_prompts:
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- "Be truthful and objective in your response. Avoid speculation or making claims that cannot be verified."
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- "Focus on providing factual information based on the evidence presented in the source material."
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- "If you are unsure about something, it's okay to say 'I don't know' or 'I can't find information to support that claim'."
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- "Be aware of potential biases in the source material and strive to present a neutral perspective."
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- "If a source seems biased, identify the bias and consider alternative viewpoints."
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- "Avoid using language that promotes stereotypes or prejudices."
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- "Cite your sources when referencing information from external materials."
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- "Acknowledge the limitations of your knowledge and the potential for different interpretations."
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- "Be transparent about your confidence level in your answer."
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- "Explain your reasoning process and how you arrived at your answer."
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- "Provide evidence to support your claims whenever possible."
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- "If there are multiple perspectives on an issue, present them fairly and objectively."
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- source_model: senseable/WestLake-7B-v2
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positive_prompts:
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- "This sentence contains a pronoun ('he' or 'she'). Pay close attention to the context to determine who the pronoun refers to."
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- "Identify the two potential referents for the pronoun ('he' or 'she') in this sentence. Analyze the context to choose the correct one."
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- "This scenario describes two individuals. Use the information provided to understand who the pronoun refers to in the sentence."
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- "Focus on the actions described in the sentence and the roles of the individuals involved. This will help determine the pronoun referent."
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- "Analyze the relationship between the individuals mentioned in the sentence. The pronoun likely refers to the one performing the action."
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- "Consider the animacy of the potential referents. Pronouns typically refer to animate beings (people or animals) in the context."
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- "Don't rely solely on the pronoun itself. Utilize the entire sentence and surrounding context to understand its meaning."
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- "Look for clues in the sentence that indicate who the pronoun refers to. This could include gender, possession, or actions described."
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- "Imagine the scenario described in the sentence. Visualizing the situation can help you identify the intended referent."
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- "Evaluate the plausibility of each potential referent for the pronoun. Choose the one that makes the most logical sense in the context."
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- "Think about the actions described and the roles of the individuals involved. Does it make more sense for one or the other to perform the action?"
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- "Consider the world knowledge you possess. Does the sentence describe a situation where one referent is more likely than the other?"
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133
README.md
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README.md
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---
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license: apache-2.0
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model-index:
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- name: mera-mix-4x7B
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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: 72.95
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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=meraGPT/mera-mix-4x7B
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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:
|
||||
- type: acc_norm
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||||
value: 89.17
|
||||
name: normalized accuracy
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||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B
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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:
|
||||
- type: acc
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||||
value: 64.44
|
||||
name: accuracy
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||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B
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||||
name: Open LLM Leaderboard
|
||||
- task:
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||||
type: text-generation
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||||
name: Text Generation
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||||
dataset:
|
||||
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:
|
||||
- type: mc2
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||||
value: 77.17
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||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B
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||||
name: Open LLM Leaderboard
|
||||
- task:
|
||||
type: text-generation
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||||
name: Text Generation
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||||
dataset:
|
||||
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:
|
||||
num_few_shot: 5
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||||
metrics:
|
||||
- type: acc
|
||||
value: 85.64
|
||||
name: accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B
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||||
name: Open LLM Leaderboard
|
||||
- task:
|
||||
type: text-generation
|
||||
name: Text Generation
|
||||
dataset:
|
||||
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:
|
||||
num_few_shot: 5
|
||||
metrics:
|
||||
- type: acc
|
||||
value: 66.11
|
||||
name: accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B
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||||
name: Open LLM Leaderboard
|
||||
---
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||||
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# Model mera-mix-4x7B
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||||
This is a mixture of experts (MoE) model that is half as large (4 experts instead of 8) as the [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
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while been comparable to it across different benchmarks. You can use it as a drop in replacement for your Mixtral-8x7B and get much faster inference.
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mera-mix-4x7B achieves the score of 75.91 on the OpenLLM Eval and compares well with 72.7 by Mixtral-8x7B and 74.46 by Mixtral-8x22B.
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You can try the model with the [Mera Mixture Chat](https://huggingface.co/spaces/meraGPT/mera-mixture-chat).
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In addition, to the official Open LLM Leaderboard, the results on OpenLLM Eval have been validated by [others as well (76.59)](https://github.com/saucam/model_evals/tree/main?tab=readme-ov-file#model-eval-results).
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||||
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Our own initial eval is available [here (76.37)](https://gist.github.com/codelion/78f88333230801c9bbaa6fc22078d820).
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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_meraGPT__mera-mix-4x7B)
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| Metric |Value|
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|---------------------------------|----:|
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||||
|Avg. |75.91|
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|AI2 Reasoning Challenge (25-Shot)|72.95|
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|HellaSwag (10-Shot) |89.17|
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|MMLU (5-Shot) |64.44|
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|TruthfulQA (0-shot) |77.17|
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|Winogrande (5-shot) |85.64|
|
||||
|GSM8k (5-shot) |66.11|
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5
added_tokens.json
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added_tokens.json
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{
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||||
"<sep>": 32002,
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||||
"<|end_of_turn|>": 32000,
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||||
"<|pad_0|>": 32001
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||||
}
|
||||
30
config.json
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30
config.json
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{
|
||||
"_name_or_path": "chihoonlee10/T3Q-EN-DPO-Mistral-7B",
|
||||
"architectures": [
|
||||
"MixtralForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 14336,
|
||||
"max_position_embeddings": 32768,
|
||||
"model_type": "mixtral",
|
||||
"num_attention_heads": 32,
|
||||
"num_experts_per_tok": 2,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
||||
"num_local_experts": 4,
|
||||
"output_router_logits": false,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_theta": 10000.0,
|
||||
"router_aux_loss_coef": 0.001,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.39.3",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32000
|
||||
}
|
||||
71
mergekit_moe_config.yml
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71
mergekit_moe_config.yml
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@@ -0,0 +1,71 @@
|
||||
base_model: chihoonlee10/T3Q-EN-DPO-Mistral-7B
|
||||
gate_mode: hidden # one of "hidden", "cheap_embed", or "random"
|
||||
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
|
||||
## (optional)
|
||||
experts_per_token: 2
|
||||
experts:
|
||||
- source_model: Kukedlc/Jupiter-k-7B-slerp
|
||||
positive_prompts:
|
||||
- "This puzzle involves identifying a repeating pattern. Can you analyze the examples and complete the missing element?"
|
||||
- "These grids seem to follow a specific visual pattern. Can you analyze the examples and apply the pattern to solve the puzzle?"
|
||||
- "Look for a consistent rule in how the elements are arranged. Can you use that rule to predict the missing element?"
|
||||
- "This task requires applying logical deduction. Can you analyze the information and choose the answer that follows logically?"
|
||||
- "Reason through the cause-and-effect relationships in the examples. Can you use that logic to solve the puzzle?"
|
||||
- "Analyze the relationship between the input and output in the examples. Can you apply that relationship to solve the new problem?"
|
||||
- "This puzzle requires modifying the image based on a specific rule. Can you analyze the changes in the examples and apply them to solve the puzzle?"
|
||||
- "Focus on the visual changes demonstrated in the training examples. Can you replicate those changes to solve the new problem?"
|
||||
- "This task involves manipulating shapes or colors based on a pattern. Can you analyze the examples and apply the pattern to the new image?"
|
||||
- "These puzzles involve manipulating numbers according to a specific rule. Can you analyze the pattern and solve the missing number?"
|
||||
- "Focus on the mathematical operations demonstrated in the examples. Can you apply those operations to solve the new equation?"
|
||||
- "Look for relationships between the numbers in the training examples. Can you use that relationship to predict the missing number?"
|
||||
- "This task requires understanding the arrangement of objects in space. Can you analyze the movement patterns in the examples and predict the next step?"
|
||||
- "Focus on the spatial relationships between elements in the grids. Can you replicate those relationships to solve the new puzzle?"
|
||||
- "Analyze the rotation, reflection, or translation demonstrated in the examples. Can you apply that manipulation to solve the new problem?"
|
||||
- source_model: InferenceIllusionist/Excalibur-7b-DPO
|
||||
positive_prompts:
|
||||
- "This passage contains factual information. Can you summarize the key details about [topic]?"
|
||||
- "Based on the information provided, what can you tell me about [entity]?"
|
||||
- "Is the following statement true or false according to the passage: [statement]?"
|
||||
- "What caused [event] to happen in the passage?"
|
||||
- "Why did [character] take the action of [action]?"
|
||||
- "If [condition] were true, what would likely happen next?"
|
||||
- "How are [entity A] and [entity B] similar/different?"
|
||||
- "Which option, [A] or [B], is more likely based on the information provided?"
|
||||
- "Rank the following options ([list]) based on [criteria] according to the passage."
|
||||
- "Does the following statement logically follow from the information provided: [statement]?"
|
||||
- "Identify any inconsistencies or contradictions in the passage."
|
||||
- "Can you draw a logical conclusion based on the evidence presented?"
|
||||
- "What can be inferred about [concept] based on the information provided?"
|
||||
- "What is the underlying meaning or implication of the author's statement?"
|
||||
- "Can you fill in the blanks with the most likely word(s) based on the context?"
|
||||
- "What are some potential consequences of [event]?"
|
||||
- "Can you generate creative solutions to the problem presented in the passage?"
|
||||
- "Based on the information provided, propose a course of action for [character]."
|
||||
- source_model: yam-peleg/Experiment21-7B
|
||||
positive_prompts:
|
||||
- "Be truthful and objective in your response. Avoid speculation or making claims that cannot be verified."
|
||||
- "Focus on providing factual information based on the evidence presented in the source material."
|
||||
- "If you are unsure about something, it's okay to say 'I don't know' or 'I can't find information to support that claim'."
|
||||
- "Be aware of potential biases in the source material and strive to present a neutral perspective."
|
||||
- "If a source seems biased, identify the bias and consider alternative viewpoints."
|
||||
- "Avoid using language that promotes stereotypes or prejudices."
|
||||
- "Cite your sources when referencing information from external materials."
|
||||
- "Acknowledge the limitations of your knowledge and the potential for different interpretations."
|
||||
- "Be transparent about your confidence level in your answer."
|
||||
- "Explain your reasoning process and how you arrived at your answer."
|
||||
- "Provide evidence to support your claims whenever possible."
|
||||
- "If there are multiple perspectives on an issue, present them fairly and objectively."
|
||||
- source_model: senseable/WestLake-7B-v2
|
||||
positive_prompts:
|
||||
- "This sentence contains a pronoun ('he' or 'she'). Pay close attention to the context to determine who the pronoun refers to."
|
||||
- "Identify the two potential referents for the pronoun ('he' or 'she') in this sentence. Analyze the context to choose the correct one."
|
||||
- "This scenario describes two individuals. Use the information provided to understand who the pronoun refers to in the sentence."
|
||||
- "Focus on the actions described in the sentence and the roles of the individuals involved. This will help determine the pronoun referent."
|
||||
- "Analyze the relationship between the individuals mentioned in the sentence. The pronoun likely refers to the one performing the action."
|
||||
- "Consider the animacy of the potential referents. Pronouns typically refer to animate beings (people or animals) in the context."
|
||||
- "Don't rely solely on the pronoun itself. Utilize the entire sentence and surrounding context to understand its meaning."
|
||||
- "Look for clues in the sentence that indicate who the pronoun refers to. This could include gender, possession, or actions described."
|
||||
- "Imagine the scenario described in the sentence. Visualizing the situation can help you identify the intended referent."
|
||||
- "Evaluate the plausibility of each potential referent for the pronoun. Choose the one that makes the most logical sense in the context."
|
||||
- "Think about the actions described and the roles of the individuals involved. Does it make more sense for one or the other to perform the action?"
|
||||
- "Consider the world knowledge you possess. Does the sentence describe a situation where one referent is more likely than the other?"
|
||||
3
model-00001-of-00005.safetensors
Normal file
3
model-00001-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b3391957ad1fcacac319678cbc5d2a3871e854c8eb5abedf19d46d8a4c0fba14
|
||||
size 9919666872
|
||||
3
model-00002-of-00005.safetensors
Normal file
3
model-00002-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1f41a8f5ae493bf80ce115a3c436537dae54ed8a09540d80a5103251c432147f
|
||||
size 9898670488
|
||||
3
model-00003-of-00005.safetensors
Normal file
3
model-00003-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2d6695ac15e3bf03106ae33f695710a8d8807d227010123e2e3e5e8759211972
|
||||
size 9982573720
|
||||
3
model-00004-of-00005.safetensors
Normal file
3
model-00004-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e227e74b71acecaf8d2572590f2861f4cd5812738ea3d437ac920c91429624cc
|
||||
size 9982573720
|
||||
3
model-00005-of-00005.safetensors
Normal file
3
model-00005-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cb15c9ac1d085517ea659cb20cebc463b4cf896586bcf9f60456a6c12da0c39c
|
||||
size 8523972544
|
||||
1
model.safetensors.index.json
Normal file
1
model.safetensors.index.json
Normal file
File diff suppressed because one or more lines are too long
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<s>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
91129
tokenizer.json
Normal file
91129
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<s>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user