Instructions to use Weyaxi/Bagel-Hermes-34B-Slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/Bagel-Hermes-34B-Slerp with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Weyaxi/Bagel-Hermes-34B-Slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Bagel-Hermes-34B-Slerp")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/Bagel-Hermes-34B-Slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Weyaxi/Bagel-Hermes-34B-Slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/Bagel-Hermes-34B-Slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Bagel-Hermes-34B-Slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Weyaxi/Bagel-Hermes-34B-Slerp

SGLang

How to use Weyaxi/Bagel-Hermes-34B-Slerp with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Weyaxi/Bagel-Hermes-34B-Slerp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Bagel-Hermes-34B-Slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Weyaxi/Bagel-Hermes-34B-Slerp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Bagel-Hermes-34B-Slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Weyaxi/Bagel-Hermes-34B-Slerp with Docker Model Runner:
```
docker model run hf.co/Weyaxi/Bagel-Hermes-34B-Slerp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bagel-Hermes-34B-Slerp

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Nous-Hermes-2-Yi-34B
bagel-dpo-34b-v0.2
nontoxic-bagel-34b-v0.2

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
      - model: bagel-dpo-34b-v0.2
        layer_range: [0, 60]
      - model: Nous-Hermes-2-Yi-34B
        layer_range: [0, 60]

merge_method: slerp
base_model: nontoxic-bagel-34b-v0.2

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
tokenizer_source: union
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.24
AI2 Reasoning Challenge (25-Shot)	70.73
HellaSwag (10-Shot)	85.68
MMLU (5-Shot)	77.29
TruthfulQA (0-shot)	67.09
Winogrande (5-shot)	84.37
GSM8k (5-shot)	66.26

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	27.06
IFEval (0-Shot)	46.03
BBH (3-Shot)	41.96
MATH Lvl 5 (4-Shot)	4.91
GPQA (0-shot)	11.30
MuSR (0-shot)	17.01
MMLU-PRO (5-shot)	41.15

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Safetensors

Model size

34B params

Tensor type

BF16

Model tree for Weyaxi/Bagel-Hermes-34B-Slerp

Quantizations

3 models

Space using Weyaxi/Bagel-Hermes-34B-Slerp 1

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

70.730
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.680
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

77.290
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

67.090
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.370
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.260
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

46.030
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

41.960