Instructions to use bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF

SGLang

How to use bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF 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 "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF" \
    --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": "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF",
		"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 "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF" \
        --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": "bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF
```

Llamacpp Quantizations of Hermes-2-Pro-Mistral-10.7B

Using llama.cpp release b2536 for quantization.

Original model: https://huggingface.co/Joseph717171/Hermes-2-Pro-Mistral-10.7B

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Description
Hermes-2-Pro-Mistral-10.7B-Q8_0.gguf	Q8_0	11.40GB	Extremely high quality, generally unneeded but max available quant.
Hermes-2-Pro-Mistral-10.7B-Q6_K.gguf	Q6_K	8.80GB	Very high quality, near perfect, recommended.
Hermes-2-Pro-Mistral-10.7B-Q5_K_M.gguf	Q5_K_M	7.59GB	High quality, very usable.
Hermes-2-Pro-Mistral-10.7B-Q5_K_S.gguf	Q5_K_S	7.39GB	High quality, very usable.
Hermes-2-Pro-Mistral-10.7B-Q5_0.gguf	Q5_0	7.39GB	High quality, older format, generally not recommended.
Hermes-2-Pro-Mistral-10.7B-Q4_K_M.gguf	Q4_K_M	6.46GB	Good quality, uses about 4.83 bits per weight.
Hermes-2-Pro-Mistral-10.7B-Q4_K_S.gguf	Q4_K_S	6.11GB	Slightly lower quality with small space savings.
Hermes-2-Pro-Mistral-10.7B-IQ4_NL.gguf	IQ4_NL	6.14GB	Decent quality, similar to Q4_K_S, new method of quanting,
Hermes-2-Pro-Mistral-10.7B-IQ4_XS.gguf	IQ4_XS	5.82GB	Decent quality, new method with similar performance to Q4.
Hermes-2-Pro-Mistral-10.7B-Q4_0.gguf	Q4_0	6.07GB	Decent quality, older format, generally not recommended.
Hermes-2-Pro-Mistral-10.7B-Q3_K_L.gguf	Q3_K_L	5.65GB	Lower quality but usable, good for low RAM availability.
Hermes-2-Pro-Mistral-10.7B-Q3_K_M.gguf	Q3_K_M	5.19GB	Even lower quality.
Hermes-2-Pro-Mistral-10.7B-IQ3_M.gguf	IQ3_M	4.84GB	Medium-low quality, new method with decent performance.
Hermes-2-Pro-Mistral-10.7B-IQ3_S.gguf	IQ3_S	4.69GB	Lower quality, new method with decent performance, recommended over Q3 quants.
Hermes-2-Pro-Mistral-10.7B-Q3_K_S.gguf	Q3_K_S	4.66GB	Low quality, not recommended.
Hermes-2-Pro-Mistral-10.7B-Q2_K.gguf	Q2_K	4.00GB	Extremely low quality, not recommended.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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Hardware compatibility

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6-bit

8-bit

Model tree for bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF

Base model

mistralai/Mistral-7B-v0.1

Quantized

(187)

this model

bartowski
/

Hermes-2-Pro-Mistral-10.7B-GGUF

Llamacpp Quantizations of Hermes-2-Pro-Mistral-10.7B

Model tree for bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF

Dataset used to train bartowski/Hermes-2-Pro-Mistral-10.7B-GGUF