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| Name | Quant method | Size |
|---|---|---|
| SuperMente-7B-v4.Q2_K.gguf | Q2_K | 2.53GB |
| SuperMente-7B-v4.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| SuperMente-7B-v4.IQ3_S.gguf | IQ3_S | 2.96GB |
| SuperMente-7B-v4.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| SuperMente-7B-v4.IQ3_M.gguf | IQ3_M | 3.06GB |
| SuperMente-7B-v4.Q3_K.gguf | Q3_K | 3.28GB |
| SuperMente-7B-v4.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| SuperMente-7B-v4.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| SuperMente-7B-v4.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| SuperMente-7B-v4.Q4_0.gguf | Q4_0 | 3.83GB |
| SuperMente-7B-v4.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| SuperMente-7B-v4.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| SuperMente-7B-v4.Q4_K.gguf | Q4_K | 4.07GB |
| SuperMente-7B-v4.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| SuperMente-7B-v4.Q4_1.gguf | Q4_1 | 4.24GB |
| SuperMente-7B-v4.Q5_0.gguf | Q5_0 | 4.65GB |
| SuperMente-7B-v4.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| SuperMente-7B-v4.Q5_K.gguf | Q5_K | 4.78GB |
| SuperMente-7B-v4.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| SuperMente-7B-v4.Q5_1.gguf | Q5_1 | 5.07GB |
| SuperMente-7B-v4.Q6_K.gguf | Q6_K | 5.53GB |
| SuperMente-7B-v4.Q8_0.gguf | Q8_0 | 7.17GB |
1models:
2 - model: Kukedlc/NeuralSirKrishna-7b
3 parameters:
4 density: [1, 0.7, 0.1] # density gradient
5 weight: 1.0
6 - model: Kukedlc/NeuralKybalion-7B-slerp-v3
7 parameters:
8 density: 0.5
9 weight: [0, 0.3, 0.7, 1] # weight gradient
10 - model: Kukedlc/SuperMente-7B-v3
11 parameters:
12 density: 0.33
13 weight:
14 - filter: mlp
15 value: 0.5
16 - value: 0
17merge_method: ties
18base_model: Kukedlc/NeuralSirKrishna-7b
19parameters:
20 normalize: true
21 int8_mask: true
22dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Kukedlc/SuperMente-7B-v4"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])