HermesBagel-34B-v0.1

HermesBagel-34B-v0.1

HermesBagel-34B-v0.1 is a merge of the following models using LazyMergekit:

• NousResearch/Nous-Hermes-2-Yi-34B
• jondurbin/bagel-dpo-34b-v0.2

🧩 Configuration

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>

!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>

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)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here