gguf endpoints_compatible region:us
---
{}
---
Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


COKAL-v1-70B - GGUF
- Model creator: https://huggingface.co/DopeorNope/
- Original model: https://huggingface.co/DopeorNope/COKAL-v1-70B/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [COKAL-v1-70B.Q2_K.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q2_K.gguf) | Q2_K | 23.96GB |
| [COKAL-v1-70B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.IQ3_XS.gguf) | IQ3_XS | 26.64GB |
| [COKAL-v1-70B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.IQ3_S.gguf) | IQ3_S | 28.14GB |
| [COKAL-v1-70B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q3_K_S.gguf) | Q3_K_S | 28.14GB |
| [COKAL-v1-70B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.IQ3_M.gguf) | IQ3_M | 29.09GB |
| [COKAL-v1-70B.Q3_K.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q3_K.gguf) | Q3_K | 31.26GB |
| [COKAL-v1-70B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q3_K_M.gguf) | Q3_K_M | 31.26GB |
| [COKAL-v1-70B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q3_K_L.gguf) | Q3_K_L | 33.94GB |
| [COKAL-v1-70B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.IQ4_XS.gguf) | IQ4_XS | 34.94GB |
| [COKAL-v1-70B.Q4_0.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q4_0.gguf) | Q4_0 | 36.5GB |
| [COKAL-v1-70B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.IQ4_NL.gguf) | IQ4_NL | 36.86GB |
| [COKAL-v1-70B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/blob/main/COKAL-v1-70B.Q4_K_S.gguf) | Q4_K_S | 36.86GB |
| [COKAL-v1-70B.Q4_K.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q4_K | 38.88GB |
| [COKAL-v1-70B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q4_K_M | 38.88GB |
| [COKAL-v1-70B.Q4_1.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q4_1 | 40.52GB |
| [COKAL-v1-70B.Q5_0.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q5_0 | 44.53GB |
| [COKAL-v1-70B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q5_K_S | 44.53GB |
| [COKAL-v1-70B.Q5_K.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q5_K | 45.73GB |
| [COKAL-v1-70B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q5_K_M | 45.73GB |
| [COKAL-v1-70B.Q5_1.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q5_1 | 48.54GB |
| [COKAL-v1-70B.Q6_K.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q6_K | 53.06GB |
| [COKAL-v1-70B.Q8_0.gguf](https://huggingface.co/RichardErkhov/DopeorNope_-_COKAL-v1-70B-gguf/tree/main/) | Q8_0 | 68.72GB |




Original model description:
---
license: apache-2.0
---
# **🐻‍❄️COKAL-v1_70B🐻‍❄️**  
![img](./COKAL-DPO_bear.png)  

## Model Details

**Model Developers** Seungyoo Lee (DopeorNope)

**Input** Models input text only.

**Output** Models generate text only.

**Model Architecture**  
COKAL-v1_70B is an auto-regressive 70B language model based on the LLaMA2 transformer architecture.

**Base Model**  



**Training Dataset**  

- SFT training dataset: [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)

  
**Training**  
I developed the model in an environment with A100 x 8 


# Implementation Code
```python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "DopeorNope/COKAL-v1_70B"
model = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)
```

---

