Instructions to use Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B with Transformers:

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

pipe = pipeline("text-generation", model="Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B

SGLang

How to use Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B 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/OpenHermes-2.5-neural-chat-7b-v3-2-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/OpenHermes-2.5-neural-chat-7b-v3-2-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B with Docker Model Runner:
```
docker model run hf.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merge of teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-2 using ties merge.

Note: Intel/neural-chat-7b-v3-1 merge version is available here

Weights

teknium/OpenHermes-2.5-Mistral-7B: 0.5
Intel/neural-chat-7b-v3-2: 0.3

Density

teknium/OpenHermes-2.5-Mistral-7B: 0.5
Intel/neural-chat-7b-v3-2: 0.5

Prompt Templates

You can use these prompt templates, but I recommend using ChatML.

ChatML (OpenHermes-2.5-Mistral-7B):

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>

neural-chat-7b-v3-2

### System:
{system}
### User:
{user}
### Assistant:

Quantizationed versions

Quantizationed versions of this model is available thanks to TheBloke.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.71
AI2 Reasoning Challenge (25-Shot)	66.38
HellaSwag (10-Shot)	84.11
MMLU (5-Shot)	62.84
TruthfulQA (0-shot)	63.59
Winogrande (5-shot)	78.53
GSM8k (5-shot)	56.79

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Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B

Merges

2 models

Quantizations

6 models

Dataset used to train Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B

Spaces using Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B 26

Collection including Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-2-7B

Merge models

Collection

Merge models created with mergekit • 19 items • Updated Dec 3, 2025 • 3

Evaluation results

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

66.380
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.110
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.840
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

63.590
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.530
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

56.790