Instructions to use ajibawa-2023/SlimOrca-Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/SlimOrca-Llama-3-8B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/SlimOrca-Llama-3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/SlimOrca-Llama-3-8B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/SlimOrca-Llama-3-8B", device_map="auto")
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 Settings

vLLM

How to use ajibawa-2023/SlimOrca-Llama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/SlimOrca-Llama-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/SlimOrca-Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ajibawa-2023/SlimOrca-Llama-3-8B

SGLang

How to use ajibawa-2023/SlimOrca-Llama-3-8B 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 "ajibawa-2023/SlimOrca-Llama-3-8B" \
    --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": "ajibawa-2023/SlimOrca-Llama-3-8B",
		"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 "ajibawa-2023/SlimOrca-Llama-3-8B" \
        --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": "ajibawa-2023/SlimOrca-Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ajibawa-2023/SlimOrca-Llama-3-8B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/SlimOrca-Llama-3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SlimOrca-Llama-3-8B: A General Purpose Intelligent Model

This Model is trained on refined version of SlimOrca made available by Open-Orca team. This Model is very good in various types of General Purpose content generation such as Q&A (including multiple choice), Articles from Summary, Sentiment Analysis, Context & Hypothesis, Reviews, Erotic story generation etc. To a certain extent it can also generate Uncensored content. Kindly be careful while generating Uncensored content as you will be responsible for what you generate.

It is trained on 517981 set of conversations. Each set having 2 conversations. I have shared this data.

I have used ChatML prompt format.

All the credit goes to the Open-Orca team for releasing SlimOrca dataset.

Check examples given below.

Training:

Entire dataset was trained on 4 x A100 80GB. For 2 epoch, training took almost 114 hours. Axolotl & DeepSpeed codebase was used for training purpose. Entire data is trained on Llama-3 by Meta.

This is a fully fine tuned model. Links for quantized models are given below.

GGUF & Exllama

GGUF: Link

Exllama: Link

Special Thanks to Bartowski for quantizing my model.

Example Prompt:

This model uses ChatML prompt format.

<|im_start|>system
You are a helpful Assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

You can modify above Prompt as per your requirement.

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Examples

Example 1

Example 2

Example 3

Example 4

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Safetensors

Model size

8B params

Tensor type

BF16

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Quantizations

3 models

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