---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Nous-Hermes-2-Yi-34B
- jondurbin/bagel-dpo-34b-v0.2
base_model:
- NousResearch/Nous-Hermes-2-Yi-34B
- jondurbin/bagel-dpo-34b-v0.2
---


# HermesBagel-34B-v0.1

HermesBagel-34B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
* [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: NousResearch/Nous-Hermes-2-Yi-34B
        layer_range: [0, 60]
      - model: jondurbin/bagel-dpo-34b-v0.2
        layer_range: [0, 60]
merge_method: slerp
base_model: NousResearch/Nous-Hermes-2-Yi-34B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```

## Basic Usage

<details>

<summary>Setup</summary>

```python
!pip install -qU transformers accelerate bitsandbytes

from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    BitsAndBytesConfig
)
import torch

model = "dfurman/HermesBagel-34B-v0.1"
nf4_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(
    model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=nf4_config,
)
```

</details>


```python
messages = [
    {"role": "user", "content": "What is a large language model?"},
]

print("\n\n*** Prompt:")
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt",
)
print(tokenizer.decode(input_ids[0]))

print("\n\n*** Generate:")
with torch.autocast("cuda", dtype=torch.bfloat16):
    output = model.generate(
        input_ids=input_ids.to("cuda"),
        max_new_tokens=256,
        return_dict_in_generate=True,
    )

response = tokenizer.decode(
    output["sequences"][0][len(input_ids[0]):], 
    skip_special_tokens=True
)
print(response)
```