Instructions to use hyperspaceai/hyperEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyperspaceai/hyperEngine with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hyperspaceai/hyperEngine")
model = AutoModelForCausalLM.from_pretrained("hyperspaceai/hyperEngine")
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

vLLM

How to use hyperspaceai/hyperEngine with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hyperspaceai/hyperEngine

SGLang

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

Docker Model Runner
How to use hyperspaceai/hyperEngine with Docker Model Runner:
```
docker model run hf.co/hyperspaceai/hyperEngine
```

hyperEngine / README.md

parthhyperspace

Update README.md

4f6f6bd verified about 2 years ago

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metadata

language:
  - en
license: apache-2.0
base_model: alpindale/Mistral-7B-v0.2-hf
datasets:
  - cognitivecomputations/dolphin
  - cognitivecomputations/dolphin-coder
  - cognitivecomputations/samantha-data
  - jondurbin/airoboros-2.2.1
  - teknium/openhermes-2.5
  - m-a-p/Code-Feedback
  - m-a-p/CodeFeedback-Filtered-Instruction
model-index:
  - name: dolphin-2.8-mistral-7b-v02
    results:
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: openai_humaneval
        metrics:
          - type: pass@1
            value: 0.469
            name: pass@1
            verified: false
pipeline_tag: text-generation

hyperspaceai/hyperEngine

This model was converted to MLX format from cognitivecomputations/dolphin-2.8-mistral-7b-v02 using mlx-lm version 0.9.0. Refer to the original model card for more details on the model.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("hyperspaceai/hyperEngine")
response = generate(model, tokenizer, prompt="hello", verbose=True)