Instructions to use rohansolo/bbdeci7b-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rohansolo/bbdeci7b-sft-lora with Transformers:

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

pipe = pipeline("text-generation", model="rohansolo/bbdeci7b-sft-lora", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("rohansolo/bbdeci7b-sft-lora", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rohansolo/bbdeci7b-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rohansolo/bbdeci7b-sft-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rohansolo/bbdeci7b-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rohansolo/bbdeci7b-sft-lora

SGLang

How to use rohansolo/bbdeci7b-sft-lora 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 "rohansolo/bbdeci7b-sft-lora" \
    --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": "rohansolo/bbdeci7b-sft-lora",
		"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 "rohansolo/bbdeci7b-sft-lora" \
        --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": "rohansolo/bbdeci7b-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rohansolo/bbdeci7b-sft-lora with Docker Model Runner:
```
docker model run hf.co/rohansolo/bbdeci7b-sft-lora
```

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bbdeci7b-sft-lora

This model is a fine-tuned version of Deci/DeciLM-7B on HuggingFaceH4/ultrachat_200k It achieves the following results on the evaluation set:

Loss: 1.0110

Model description

more information to come soon

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 128
total_train_batch_size: 1024
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.0062	1.00	136	1.0110

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for rohansolo/bbdeci7b-sft-lora

Base model

Deci/DeciLM-7B

Finetuned

(3)

this model

rohansolo
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bbdeci7b-sft-lora