Instructions to use RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits

SGLang

How to use RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits 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 "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits" \
    --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": "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits",
		"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 "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits" \
        --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": "RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Kquant03_-_Hippolyta-7B-bf16-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Hippolyta-7B-bf16 - bnb 8bits

Model creator: https://huggingface.co/Kquant03/
Original model: https://huggingface.co/Kquant03/Hippolyta-7B-bf16/

Original model description:

license: apache-2.0 datasets: - Open-Orca/OpenOrca - teknium/openhermes - cognitivecomputations/dolphin - jondurbin/airoboros-3.1 - unalignment/toxic-dpo-v0.1 - unalignment/spicy-3.1 language: - en

The flower of Ares.

GGUF files here

Fine-tuned on mistralai/Mistral-7B-v0.1...my team and I reformatted many different datasets and included a small amount of private stuff to see how much we could improve mistral.

I spoke to it personally for about an hour, and I believe we need to work on our format for the private dataset a bit more, but other than that, it turned out great. I will be uploading it to open llm evaluations, today.

Uses Mistral prompt template with chat-instruct.

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F32

F16