Instructions to use jaigouk/ruby-una-cybertron-7b-v2-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaigouk/ruby-una-cybertron-7b-v2-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="jaigouk/ruby-una-cybertron-7b-v2-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaigouk/ruby-una-cybertron-7b-v2-bf16")
model = AutoModelForCausalLM.from_pretrained("jaigouk/ruby-una-cybertron-7b-v2-bf16")
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 jaigouk/ruby-una-cybertron-7b-v2-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaigouk/ruby-una-cybertron-7b-v2-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaigouk/ruby-una-cybertron-7b-v2-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaigouk/ruby-una-cybertron-7b-v2-bf16

SGLang

How to use jaigouk/ruby-una-cybertron-7b-v2-bf16 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 "jaigouk/ruby-una-cybertron-7b-v2-bf16" \
    --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": "jaigouk/ruby-una-cybertron-7b-v2-bf16",
		"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 "jaigouk/ruby-una-cybertron-7b-v2-bf16" \
        --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": "jaigouk/ruby-una-cybertron-7b-v2-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaigouk/ruby-una-cybertron-7b-v2-bf16 with Docker Model Runner:
```
docker model run hf.co/jaigouk/ruby-una-cybertron-7b-v2-bf16
```

ruby-una-cybertron-7b-v2-bf16

This model is a fine-tuned version of fblgit/una-cybertron-7b-v2-bf16. It achieves the following results on the evaluation set:

Loss: 0.6878

Model description

intention is to use this with system message for generating ruby codes

Intended uses & limitations

writing rspec / ruby codes

Training and evaluation data

https://huggingface.co/datasets/jaigouk/coding-dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.2904	2.53	100	0.6878

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.14.1

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for jaigouk/ruby-una-cybertron-7b-v2-bf16

Base model

fblgit/una-cybertron-7b-v2-bf16

Finetuned

jaigouk/trained-una-cybertron-7b-v2-bf16

Finetuned

(1)

this model