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| Name | Quant method | Size |
|---|---|---|
| NeuralMona_MoE-4x7B.Q2_K.gguf | Q2_K | 8.24GB |
| NeuralMona_MoE-4x7B.IQ3_XS.gguf | IQ3_XS | 9.21GB |
| NeuralMona_MoE-4x7B.IQ3_S.gguf | IQ3_S | 9.73GB |
| NeuralMona_MoE-4x7B.Q3_K_S.gguf | Q3_K_S | 9.72GB |
| NeuralMona_MoE-4x7B.IQ3_M.gguf | IQ3_M | 9.92GB |
| NeuralMona_MoE-4x7B.Q3_K.gguf | Q3_K | 10.79GB |
| NeuralMona_MoE-4x7B.Q3_K_M.gguf | Q3_K_M | 10.79GB |
| NeuralMona_MoE-4x7B.Q3_K_L.gguf | Q3_K_L | 11.68GB |
| NeuralMona_MoE-4x7B.IQ4_XS.gguf | IQ4_XS | 12.15GB |
| NeuralMona_MoE-4x7B.Q4_0.gguf | Q4_0 | 12.69GB |
| NeuralMona_MoE-4x7B.IQ4_NL.gguf | IQ4_NL | 12.81GB |
| NeuralMona_MoE-4x7B.Q4_K_S.gguf | Q4_K_S | 12.8GB |
| NeuralMona_MoE-4x7B.Q4_K.gguf | Q4_K | 13.61GB |
| NeuralMona_MoE-4x7B.Q4_K_M.gguf | Q4_K_M | 13.61GB |
| NeuralMona_MoE-4x7B.Q4_1.gguf | Q4_1 | 14.09GB |
| NeuralMona_MoE-4x7B.Q5_0.gguf | Q5_0 | 15.48GB |
| NeuralMona_MoE-4x7B.Q5_K_S.gguf | Q5_K_S | 15.48GB |
| NeuralMona_MoE-4x7B.Q5_K.gguf | Q5_K | 15.96GB |
| NeuralMona_MoE-4x7B.Q5_K_M.gguf | Q5_K_M | 15.96GB |
| NeuralMona_MoE-4x7B.Q5_1.gguf | Q5_1 | 16.88GB |
| NeuralMona_MoE-4x7B.Q6_K.gguf | Q6_K | 18.46GB |
| NeuralMona_MoE-4x7B.Q8_0.gguf | Q8_0 | 23.9GB |
1base_model: CultriX/MonaTrix-v4
2dtype: bfloat16
3experts:
4 - source_model: "CultriX/MonaTrix-v4" # Historical Analysis, Geopolitics, and Economic Evaluation
5 positive_prompts:
6 - "Historic analysis"
7 - "Geopolitical impacts"
8 - "Evaluate significance"
9 - "Predict impact"
10 - "Assess consequences"
11 - "Discuss implications"
12 - "Explain geopolitical"
13 - "Analyze historical"
14 - "Examine economic"
15 - "Evaluate role"
16 - "Analyze importance"
17 - "Discuss cultural impact"
18 - "Discuss historical"
19 negative_prompts:
20 - "Compose"
21 - "Translate"
22 - "Debate"
23 - "Solve math"
24 - "Analyze data"
25 - "Forecast"
26 - "Predict"
27 - "Process"
28 - "Coding"
29 - "Programming"
30 - "Code"
31 - "Datascience"
32 - "Cryptography"
33
34 - source_model: "mlabonne/OmniTruthyBeagle-7B-v0" # Multilingual Communication and Cultural Insights
35 positive_prompts:
36 - "Describe cultural"
37 - "Explain in language"
38 - "Translate"
39 - "Compare cultural differences"
40 - "Discuss cultural impact"
41 - "Narrate in language"
42 - "Explain impact on culture"
43 - "Discuss national identity"
44 - "Describe cultural significance"
45 - "Narrate cultural"
46 - "Discuss folklore"
47 negative_prompts:
48 - "Compose"
49 - "Debate"
50 - "Solve math"
51 - "Analyze data"
52 - "Forecast"
53 - "Predict"
54 - "Coding"
55 - "Programming"
56 - "Code"
57 - "Datascience"
58 - "Cryptography"
59
60 - source_model: "CultriX/MoNeuTrix-7B-v1" # Problem Solving, Innovation, and Creative Thinking
61 positive_prompts:
62 - "Devise strategy"
63 - "Imagine society"
64 - "Invent device"
65 - "Design concept"
66 - "Propose theory"
67 - "Reason math"
68 - "Develop strategy"
69 - "Invent"
70 negative_prompts:
71 - "Translate"
72 - "Discuss"
73 - "Debate"
74 - "Summarize"
75 - "Explain"
76 - "Detail"
77 - "Compose"
78
79 - source_model: "paulml/OmniBeagleSquaredMBX-v3-7B" # Explaining Scientific Phenomena and Principles
80 positive_prompts:
81 - "Explain scientific"
82 - "Discuss impact"
83 - "Analyze potential"
84 - "Elucidate significance"
85 - "Summarize findings"
86 - "Detail explanation"
87 negative_prompts:
88 - "Cultural significance"
89 - "Engage in creative writing"
90 - "Perform subjective judgment tasks"
91 - "Discuss cultural traditions"
92 - "Write review"
93 - "Design"
94 - "Create"
95 - "Narrate"
96 - "Discuss"1!pip install -qU transformers bitsandbytes accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "CultriX/NeuralMona_MoE-4x7B"
8
9tokenizer = AutoTokenizer.from_pretrained(model)
10pipeline = transformers.pipeline(
11 "text-generation",
12 model=model,
13 model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
14)
15
16messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
17prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
18outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
19print(outputs[0]["generated_text"])