Instructions to use giraffe176/Open_Hermes_Orca_Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use giraffe176/Open_Hermes_Orca_Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="giraffe176/Open_Hermes_Orca_Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("giraffe176/Open_Hermes_Orca_Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("giraffe176/Open_Hermes_Orca_Mistral-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use giraffe176/Open_Hermes_Orca_Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "giraffe176/Open_Hermes_Orca_Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "giraffe176/Open_Hermes_Orca_Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/giraffe176/Open_Hermes_Orca_Mistral-7B

SGLang

How to use giraffe176/Open_Hermes_Orca_Mistral-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 "giraffe176/Open_Hermes_Orca_Mistral-7B" \
    --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": "giraffe176/Open_Hermes_Orca_Mistral-7B",
		"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 "giraffe176/Open_Hermes_Orca_Mistral-7B" \
        --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": "giraffe176/Open_Hermes_Orca_Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use giraffe176/Open_Hermes_Orca_Mistral-7B with Docker Model Runner:
```
docker model run hf.co/giraffe176/Open_Hermes_Orca_Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

.samplemodel

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

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using teknium/OpenHermes-2.5-Mistral-7B as a base.

Models Merged

The following models were included in the merge:

Open-Orca/Mistral-7B-OpenOrca

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 1.0
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      weight: 0.6
merge_method: task_arithmetic
base_model: teknium/OpenHermes-2.5-Mistral-7B
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.87
AI2 Reasoning Challenge (25-Shot)	64.68
HellaSwag (10-Shot)	84.63
MMLU (5-Shot)	63.93
TruthfulQA (0-shot)	53.34
Winogrande (5-shot)	78.45
GSM8k (5-shot)	56.18

Downloads last month: 68

Safetensors

Model size

7B params

Tensor type

F16

Model tree for giraffe176/Open_Hermes_Orca_Mistral-7B

Quantizations

1 model

Paper for giraffe176/Open_Hermes_Orca_Mistral-7B

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 8

Evaluation results

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

64.680
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.630
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.930
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

53.340
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.450
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

56.180