Instructions to use lodrick-the-lafted/Hermes-Instruct-7B-100K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lodrick-the-lafted/Hermes-Instruct-7B-100K with Transformers:

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

pipe = pipeline("text-generation", model="lodrick-the-lafted/Hermes-Instruct-7B-100K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lodrick-the-lafted/Hermes-Instruct-7B-100K")
model = AutoModelForCausalLM.from_pretrained("lodrick-the-lafted/Hermes-Instruct-7B-100K", device_map="auto")
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 Settings

vLLM

How to use lodrick-the-lafted/Hermes-Instruct-7B-100K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lodrick-the-lafted/Hermes-Instruct-7B-100K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lodrick-the-lafted/Hermes-Instruct-7B-100K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lodrick-the-lafted/Hermes-Instruct-7B-100K

SGLang

How to use lodrick-the-lafted/Hermes-Instruct-7B-100K 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 "lodrick-the-lafted/Hermes-Instruct-7B-100K" \
    --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": "lodrick-the-lafted/Hermes-Instruct-7B-100K",
		"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 "lodrick-the-lafted/Hermes-Instruct-7B-100K" \
        --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": "lodrick-the-lafted/Hermes-Instruct-7B-100K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lodrick-the-lafted/Hermes-Instruct-7B-100K with Docker Model Runner:
```
docker model run hf.co/lodrick-the-lafted/Hermes-Instruct-7B-100K
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Hermes-Instruct-7B-v0.2

Mistral-7B-Instruct-v0.2 trained with 100K rows of teknium/openhermes, in Alpaca format.

Prompt Format

Both the default Mistral-Instruct tags and Alpaca are fine, so either:

<s>[INST] {sys_prompt} {instruction} [/INST]

{sys_prompt}

### Instruction:
{instruction}

### Response:

The tokenizer default is Alpaca this time around.

Usage

from transformers import AutoTokenizer
import transformers
import torch

model = "lodrick-the-lafted/Hermes-Instruct-7B-100K"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16},
)

messages = [{"role": "user", "content": "Give me a cooking recipe for an apple pie."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	64.96
AI2 Reasoning Challenge (25-Shot)	61.52
HellaSwag (10-Shot)	82.84
MMLU (5-Shot)	60.95
TruthfulQA (0-shot)	63.62
Winogrande (5-shot)	76.87
GSM8k (5-shot)	43.97

Downloads last month: 51

Model tree for lodrick-the-lafted/Hermes-Instruct-7B-100K

Merges

1 model

Quantizations

2 models

Dataset used to train lodrick-the-lafted/Hermes-Instruct-7B-100K

Spaces using lodrick-the-lafted/Hermes-Instruct-7B-100K 9

Evaluation results

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

61.520
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

82.840
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

60.950
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

63.620
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

76.870
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

43.970