Instructions to use Abe13/Full-juni-Mistral-7B-OpenOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abe13/Full-juni-Mistral-7B-OpenOrca with Transformers:

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

pipe = pipeline("text-generation", model="Abe13/Full-juni-Mistral-7B-OpenOrca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abe13/Full-juni-Mistral-7B-OpenOrca")
model = AutoModelForCausalLM.from_pretrained("Abe13/Full-juni-Mistral-7B-OpenOrca")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Abe13/Full-juni-Mistral-7B-OpenOrca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abe13/Full-juni-Mistral-7B-OpenOrca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abe13/Full-juni-Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abe13/Full-juni-Mistral-7B-OpenOrca

SGLang

How to use Abe13/Full-juni-Mistral-7B-OpenOrca 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 "Abe13/Full-juni-Mistral-7B-OpenOrca" \
    --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": "Abe13/Full-juni-Mistral-7B-OpenOrca",
		"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 "Abe13/Full-juni-Mistral-7B-OpenOrca" \
        --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": "Abe13/Full-juni-Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abe13/Full-juni-Mistral-7B-OpenOrca with Docker Model Runner:
```
docker model run hf.co/Abe13/Full-juni-Mistral-7B-OpenOrca
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ChatGPT: "This iteration signifies a meticulously fine-tuned version designed to seamlessly integrate new knowledge into the model's existing framework. The primary goal is to enhance the model's understanding and performance by updating its knowledge base, all while ensuring that its pre-existing capabilities are retained and not compromised."

Downloads last month: 853

Model tree for Abe13/Full-juni-Mistral-7B-OpenOrca

Quantizations

1 model