Instructions to use LoSboccacc/orthogonal-2x7B-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoSboccacc/orthogonal-2x7B-v2-base with Transformers:

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

pipe = pipeline("text-generation", model="LoSboccacc/orthogonal-2x7B-v2-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoSboccacc/orthogonal-2x7B-v2-base")
model = AutoModelForCausalLM.from_pretrained("LoSboccacc/orthogonal-2x7B-v2-base")
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 LoSboccacc/orthogonal-2x7B-v2-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoSboccacc/orthogonal-2x7B-v2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoSboccacc/orthogonal-2x7B-v2-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LoSboccacc/orthogonal-2x7B-v2-base

SGLang

How to use LoSboccacc/orthogonal-2x7B-v2-base 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 "LoSboccacc/orthogonal-2x7B-v2-base" \
    --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": "LoSboccacc/orthogonal-2x7B-v2-base",
		"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 "LoSboccacc/orthogonal-2x7B-v2-base" \
        --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": "LoSboccacc/orthogonal-2x7B-v2-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoSboccacc/orthogonal-2x7B-v2-base with Docker Model Runner:
```
docker model run hf.co/LoSboccacc/orthogonal-2x7B-v2-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

base_model: mistralai/Mistral-7B-Instruct-v0.2 gate_mode: hidden # one of "hidden", "cheap_embed", or "random" dtype: bfloat16 # output dtype (float32, float16, or bfloat16) experts: - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B positive_prompts: - "roleplay" - source_model: mistralai/Mistral-7B-Instruct-v0.2 positive_prompts: - "chat"

chatml

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.47
AI2 Reasoning Challenge (25-Shot)	66.89
HellaSwag (10-Shot)	85.69
MMLU (5-Shot)	62.65
TruthfulQA (0-shot)	66.80
Winogrande (5-shot)	77.35
GSM8k (5-shot)	51.40

Downloads last month: 199

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for LoSboccacc/orthogonal-2x7B-v2-base

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

66.890
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.690
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.650
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.800
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.350
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

51.400