README.md

---
base_model: mlabonne/Darewin-7B-v2
inference: false
language:
- en
library_name: transformers
license: apache-2.0
merged_models:
- OpenPipe/mistral-ft-optimized-1227
- Intel/neural-chat-7b-v3-3
- openchat/openchat-3.5-0106
- openaccess-ai-collective/DPOpenHermes-7B-v2
- mlabonne/NeuralHermes-2.5-Mistral-7B
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- Open-Orca/Mistral-7B-OpenOrca
model-index:
- name: Darewin-7B-v2
  results:
  - dataset:
      args:
        num_few_shot: 25
      config: ARC-Challenge
      name: AI2 Reasoning Challenge (25-Shot)
      split: test
      type: ai2_arc
    metrics:
    - name: normalized accuracy
      type: acc_norm
      value: 62.63
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
  - dataset:
      args:
        num_few_shot: 10
      name: HellaSwag (10-Shot)
      split: validation
      type: hellaswag
    metrics:
    - name: normalized accuracy
      type: acc_norm
      value: 78.28
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
  - dataset:
      args:
        num_few_shot: 5
      config: all
      name: MMLU (5-Shot)
      split: test
      type: cais/mmlu
    metrics:
    - name: accuracy
      type: acc
      value: 53.01
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
  - dataset:
      args:
        num_few_shot: 0
      config: multiple_choice
      name: TruthfulQA (0-shot)
      split: validation
      type: truthful_qa
    metrics:
    - type: mc2
      value: 50.99
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
  - dataset:
      args:
        num_few_shot: 5
      config: winogrande_xl
      name: Winogrande (5-shot)
      split: validation
      type: winogrande
    metrics:
    - name: accuracy
      type: acc
      value: 73.95
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
  - dataset:
      args:
        num_few_shot: 5
      config: main
      name: GSM8k (5-shot)
      split: test
      type: gsm8k
    metrics:
    - name: accuracy
      type: acc
      value: 19.18
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Darewin-7B-v2
    task:
      name: Text Generation
      type: text-generation
model_creator: mlabonne
model_name: Darewin-7B-v2
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system

  {system_message}<|im_end|>

  <|im_start|>user

  {prompt}<|im_end|>

  <|im_start|>assistant

  '
quantized_by: Suparious
tags:
- merge
- mergekit
- lazymergekit
- quantized
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
- chatml
---
# mlabonne/Darewin-7B-v2 AWQ

- Model creator: [mlabonne](https://huggingface.co/mlabonne)
- Original model: [Darewin-7B-v2](https://huggingface.co/mlabonne/Darewin-7B-v2)

**Model Avatar TBD**

## Model Summary

Darewin-7B-v2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
* [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3)
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)

## How to use

### Install the necessary packages

```bash
pip install --upgrade autoawq autoawq-kernels
```

### Example Python code

```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/Darewin-7B-v2-AWQ"
system_message = "You are Darewin, incarnated as a powerful AI."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)

```

### About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

## Prompt template: ChatML

```plaintext
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```