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| Name | Quant method | Size |
|---|---|---|
| Power-WizardLM-2-13b.Q2_K.gguf | Q2_K | 4.33GB |
| Power-WizardLM-2-13b.IQ3_XS.gguf | IQ3_XS | 4.81GB |
| Power-WizardLM-2-13b.IQ3_S.gguf | IQ3_S | 5.07GB |
| Power-WizardLM-2-13b.Q3_K_S.gguf | Q3_K_S | 5.04GB |
| Power-WizardLM-2-13b.IQ3_M.gguf | IQ3_M | 5.24GB |
| Power-WizardLM-2-13b.Q3_K.gguf | Q3_K | 5.62GB |
| Power-WizardLM-2-13b.Q3_K_M.gguf | Q3_K_M | 5.62GB |
| Power-WizardLM-2-13b.Q3_K_L.gguf | Q3_K_L | 6.11GB |
| Power-WizardLM-2-13b.IQ4_XS.gguf | IQ4_XS | 6.3GB |
| Power-WizardLM-2-13b.Q4_0.gguf | Q4_0 | 6.57GB |
| Power-WizardLM-2-13b.IQ4_NL.gguf | IQ4_NL | 6.64GB |
| Power-WizardLM-2-13b.Q4_K_S.gguf | Q4_K_S | 6.62GB |
| Power-WizardLM-2-13b.Q4_K.gguf | Q4_K | 6.99GB |
| Power-WizardLM-2-13b.Q4_K_M.gguf | Q4_K_M | 6.99GB |
| Power-WizardLM-2-13b.Q4_1.gguf | Q4_1 | 7.29GB |
| Power-WizardLM-2-13b.Q5_0.gguf | Q5_0 | 8.01GB |
| Power-WizardLM-2-13b.Q5_K_S.gguf | Q5_K_S | 8.01GB |
| Power-WizardLM-2-13b.Q5_K.gguf | Q5_K | 8.22GB |
| Power-WizardLM-2-13b.Q5_K_M.gguf | Q5_K_M | 8.22GB |
| Power-WizardLM-2-13b.Q5_1.gguf | Q5_1 | 8.73GB |
| Power-WizardLM-2-13b.Q6_K.gguf | Q6_K | 9.53GB |
| Power-WizardLM-2-13b.Q8_0.gguf | Q8_0 | 12.35GB |
1slices:
2- sources:
3 - layer_range: [0, 8]
4 model: dreamgen/WizardLM-2-7B
5- sources:
6 - layer_range: [4, 12]
7 model: dreamgen/WizardLM-2-7B
8- sources:
9 - layer_range: [8, 16]
10 model: dreamgen/WizardLM-2-7B
11- sources:
12 - layer_range: [12, 20]
13 model: dreamgen/WizardLM-2-7B
14- sources:
15 - layer_range: [16, 24]
16 model: dreamgen/WizardLM-2-7B
17- sources:
18 - layer_range: [20, 28]
19 model: dreamgen/WizardLM-2-7B
20- sources:
21 - layer_range: [24, 32]
22 model: dreamgen/WizardLM-2-7B
23merge_method: passthrough
24dtype: float161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "KingNish/Power-WizardLM-2-13b"
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"])