Instructions to use xaviviro/OpenHermes-2.5-FLOR-6.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xaviviro/OpenHermes-2.5-FLOR-6.3B with Transformers:

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

pipe = pipeline("text-generation", model="xaviviro/OpenHermes-2.5-FLOR-6.3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xaviviro/OpenHermes-2.5-FLOR-6.3B")
model = AutoModelForCausalLM.from_pretrained("xaviviro/OpenHermes-2.5-FLOR-6.3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xaviviro/OpenHermes-2.5-FLOR-6.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xaviviro/OpenHermes-2.5-FLOR-6.3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xaviviro/OpenHermes-2.5-FLOR-6.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xaviviro/OpenHermes-2.5-FLOR-6.3B

SGLang

How to use xaviviro/OpenHermes-2.5-FLOR-6.3B 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 "xaviviro/OpenHermes-2.5-FLOR-6.3B" \
    --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": "xaviviro/OpenHermes-2.5-FLOR-6.3B",
		"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 "xaviviro/OpenHermes-2.5-FLOR-6.3B" \
        --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": "xaviviro/OpenHermes-2.5-FLOR-6.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xaviviro/OpenHermes-2.5-FLOR-6.3B with Docker Model Runner:
```
docker model run hf.co/xaviviro/OpenHermes-2.5-FLOR-6.3B
```

OpenHermes-2.5-FLOR-6.3B

OpenHermes-2.5-FLOR-6.3B És el resultat de finetunejar el model FLOR-6.3B amb el fantàstic dataset OpenHermes v2.5.

La millor manera d'usar OpenHermes-2.5-FLOR-6.3B és amb el format ChatML

Quantitzats

Podeu trobar el model quantitzat i en format GGUF a OpenHermes-2.5-FLOR-6.3B-GGUF

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.04
AI2 Reasoning Challenge (25-Shot)	33.45
HellaSwag (10-Shot)	54.53
MMLU (5-Shot)	25.18
TruthfulQA (0-shot)	46.12
Winogrande (5-shot)	62.98
GSM8k (5-shot)	0.00

Downloads last month: 50

Dataset used to train xaviviro/OpenHermes-2.5-FLOR-6.3B

Evaluation results

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

33.450
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

54.530
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

25.180
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

46.120
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

62.980
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000