Instructions to use bhavinjawade/nectororca-solar10b-jawade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bhavinjawade/nectororca-solar10b-jawade with Transformers:

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

pipe = pipeline("text-generation", model="bhavinjawade/nectororca-solar10b-jawade")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/nectororca-solar10b-jawade")
model = AutoModelForCausalLM.from_pretrained("bhavinjawade/nectororca-solar10b-jawade")
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 bhavinjawade/nectororca-solar10b-jawade with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bhavinjawade/nectororca-solar10b-jawade"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhavinjawade/nectororca-solar10b-jawade",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bhavinjawade/nectororca-solar10b-jawade

SGLang

How to use bhavinjawade/nectororca-solar10b-jawade 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 "bhavinjawade/nectororca-solar10b-jawade" \
    --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": "bhavinjawade/nectororca-solar10b-jawade",
		"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 "bhavinjawade/nectororca-solar10b-jawade" \
        --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": "bhavinjawade/nectororca-solar10b-jawade",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bhavinjawade/nectororca-solar10b-jawade with Docker Model Runner:
```
docker model run hf.co/bhavinjawade/nectororca-solar10b-jawade
```

SOLAR-10B-OrcaDPO-Jawade

Overview

This model card is instruction finetuned version of upstage/SOLAR-10.7B-Instruct-v1.0 model. Trained on the Intel DPO Orca dataset using LoRA. Though it should be noted SOLAR-10.7B paper states that the original model for alignment was trained on Intel ORCA DPO pairs. Retraining using DPO and LoRA shows slight (<1%) improvement on OpenLLM Leaderboard benchmarks against SOLAR 10.7B-Instruct and significant over SOLAR 10.7B

How to Use This Model

To use the model bhavinjawade/SOLAR-10B-OrcaDPO-Jawade, follow these steps:

Import and Load the Model and Tokenizer Begin by importing the model and tokenizer. Load them using the from_pretrained method.

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")

Format the Prompt Format the chat input as a list of messages, each with a role ('system' or 'user') and content.

message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"}
]
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

Create a Pipeline Set up a pipeline for text generation with the loaded model and tokenizer.

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

Generate Text Use the pipeline to generate a sequence of text based on the prompt. You can adjust parameters like temperature and top_p for different styles of responses.

sequences = pipeline(
      prompt,
      do_sample=True,
    temperature=0.7,
       top_p=0.9,
       num_return_sequences=1,
       max_length=200,
   )
 print(sequences[0]['generated_text'])

This setup allows you to utilize the capabilities of the bhavinjawade/SOLAR-10B-OrcaDPO-Jawade model for generating responses to chat inputs.

License

Type: MIT License
Details: This license permits reuse, modification, and distribution for both private and commercial purposes under the terms of the MIT License.

Model Details

Model Name: SOLAR-10.7B-Instruct-v1.0
Organization: Upstage
Training Dataset: Intel/orca_dpo_pairs
Technique Used: LoRA (Low-Rank Adaptation)

Contact Information

https://bhavinjawade.github.io

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Safetensors

Model size

11B params

Tensor type

F32

bhavinjawade
/

nectororca-solar10b-jawade