Views
No views yet
| Name | Quant method | Size |
|---|---|---|
| GetCode-slerp.Q2_K.gguf | Q2_K | 2.36GB |
| GetCode-slerp.IQ3_XS.gguf | IQ3_XS | 2.6GB |
| GetCode-slerp.IQ3_S.gguf | IQ3_S | 2.75GB |
| GetCode-slerp.Q3_K_S.gguf | Q3_K_S | 2.75GB |
| GetCode-slerp.IQ3_M.gguf | IQ3_M | 2.9GB |
| GetCode-slerp.Q3_K.gguf | Q3_K | 3.07GB |
| GetCode-slerp.Q3_K_M.gguf | Q3_K_M | 3.07GB |
| GetCode-slerp.Q3_K_L.gguf | Q3_K_L | 3.35GB |
| GetCode-slerp.IQ4_XS.gguf | IQ4_XS | 3.4GB |
| GetCode-slerp.Q4_0.gguf | Q4_0 | 3.56GB |
| GetCode-slerp.IQ4_NL.gguf | IQ4_NL | 3.58GB |
| GetCode-slerp.Q4_K_S.gguf | Q4_K_S | 3.59GB |
| GetCode-slerp.Q4_K.gguf | Q4_K | 3.8GB |
| GetCode-slerp.Q4_K_M.gguf | Q4_K_M | 3.8GB |
| GetCode-slerp.Q4_1.gguf | Q4_1 | 3.95GB |
| GetCode-slerp.Q5_0.gguf | Q5_0 | 4.33GB |
| GetCode-slerp.Q5_K_S.gguf | Q5_K_S | 4.33GB |
| GetCode-slerp.Q5_K.gguf | Q5_K | 4.45GB |
| GetCode-slerp.Q5_K_M.gguf | Q5_K_M | 4.45GB |
| GetCode-slerp.Q5_1.gguf | Q5_1 | 4.72GB |
| GetCode-slerp.Q6_K.gguf | Q6_K | 5.15GB |
| GetCode-slerp.Q8_0.gguf | Q8_0 | 6.67GB |
1slices:
2 - sources:
3 - model: codellama/CodeLlama-7b-Instruct-hf
4 layer_range: [0, 32]
5 - model: Salesforce/codegen25-7b-multi
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: codellama/CodeLlama-7b-Instruct-hf
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: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "mavihsrr/GetCode-slerp"
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"])