---
license: apache-2.0
---


## Description
This repo contains GGUF format model files for NeuralDarewin-7B.

## Files Provided
|             Name             |  Quant  | Bits | File Size |              Remark              |
| ---------------------------- | ------- | ---- | --------- | -------------------------------- |
| neuraldarewin-7b.IQ3_XXS.gguf | IQ3_XXS |  3   |  3.02 GB  | 3.06 bpw quantization            |
| neuraldarewin-7b.IQ3_S.gguf | IQ3_S |  3   |  3.18 GB  | 3.44 bpw quantization            |
| neuraldarewin-7b.IQ3_M.gguf | IQ3_M |  3   |  3.28 GB  | 3.66 bpw quantization mix        |
| neuraldarewin-7b.Q4_0.gguf | Q4_0 |  4   |  4.11 GB  | 3.56G, +0.2166 ppl               |
| neuraldarewin-7b.IQ4_NL.gguf | IQ4_NL |  4   |  4.16 GB  | 4.25 bpw non-linear quantization |
| neuraldarewin-7b.Q4_K_M.gguf | Q4_K_M |  4   |  4.37 GB  | 3.80G, +0.0532 ppl               |
| neuraldarewin-7b.Q5_K_M.gguf | Q5_K_M |  5   |  5.13 GB  | 4.45G, +0.0122 ppl               |
| neuraldarewin-7b.Q6_K.gguf | Q6_K |  6   |  5.94 GB  | 5.15G, +0.0008 ppl               |
| neuraldarewin-7b.Q8_0.gguf | Q8_0 |  8   |  7.70 GB  | 6.70G, +0.0004 ppl               |

## Parameters
|             path             |  type   |    architecture    | rope_theta | sliding_win | max_pos_embed |
| ---------------------------- | ------- | ------------------ | ---------- | ----------- | ------------- |
| mlabonne/Darewin-7B | mistral | MistralForCausalLM | 10000.0    | 4096        | 32768         |

## Benchmarks
![](https://i.ibb.co/gjKpkcj/Neural-Darewin-7-B-GGUF.png)


# Original Model Card

Darewin-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3)
* [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2)
* [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16)
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)

## 🧩 Configuration

```yaml
models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.6
      weight: 0.2
  - model: openaccess-ai-collective/DPOpenHermes-7B-v2
    parameters:
      density: 0.6
      weight: 0.1
  - model: fblgit/una-cybertron-7b-v2-bf16
    parameters:
      density: 0.6
      weight: 0.2
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.6
      weight: 0.15
  - model: OpenPipe/mistral-ft-optimized-1227
    parameters:
      density: 0.6
      weight: 0.25
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.6
      weight: 0.1
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralDarewin-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```