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| Name | Quant method | Size |
|---|---|---|
| ShadowYamshadow-7B.Q2_K.gguf | Q2_K | 2.53GB |
| ShadowYamshadow-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| ShadowYamshadow-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| ShadowYamshadow-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| ShadowYamshadow-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| ShadowYamshadow-7B.Q3_K.gguf | Q3_K | 3.28GB |
| ShadowYamshadow-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| ShadowYamshadow-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| ShadowYamshadow-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| ShadowYamshadow-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| ShadowYamshadow-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| ShadowYamshadow-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| ShadowYamshadow-7B.Q4_K.gguf | Q4_K | 4.07GB |
| ShadowYamshadow-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| ShadowYamshadow-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| ShadowYamshadow-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| ShadowYamshadow-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| ShadowYamshadow-7B.Q5_K.gguf | Q5_K | 4.78GB |
| ShadowYamshadow-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| ShadowYamshadow-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| ShadowYamshadow-7B.Q6_K.gguf | Q6_K | 5.53GB |
| ShadowYamshadow-7B.Q8_0.gguf | Q8_0 | 7.17GB |
1slices:
2 - sources:
3 - model: CorticalStack/shadow-clown-7B-slerp
4 layer_range: [0, 32]
5 - model: automerger/YamShadow-7B
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: CorticalStack/shadow-clown-7B-slerp
9parameters:
10 t:
11 - filter: self_attn
12 value: [0, 0.5, 0.3, 0.7, 1]
13 - filter: mlp
14 value: [1, 0.5, 0.7, 0.3, 0]
15 - value: 0.5
16dtype: bfloat16
17random_seed: 0
18 ```
19
20## 💻 Usage
21
22```python
23!pip install -qU transformers accelerate
24
25from transformers import AutoTokenizer
26import transformers
27import torch
28
29model = "automerger/ShadowYamshadow-7B"
30messages = [{"role": "user", "content": "What is a large language model?"}]
31
32tokenizer = AutoTokenizer.from_pretrained(model)
33prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
34pipeline = transformers.pipeline(
35 "text-generation",
36 model=model,
37 torch_dtype=torch.float16,
38 device_map="auto",
39)
40
41outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
42print(outputs[0]["generated_text"])