Instructions to use plaguss/stablelm-2-1_6-sft-disticoder-v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plaguss/stablelm-2-1_6-sft-disticoder-v01 with Transformers:

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

pipe = pipeline("text-generation", model="plaguss/stablelm-2-1_6-sft-disticoder-v01", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("plaguss/stablelm-2-1_6-sft-disticoder-v01", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use plaguss/stablelm-2-1_6-sft-disticoder-v01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "plaguss/stablelm-2-1_6-sft-disticoder-v01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plaguss/stablelm-2-1_6-sft-disticoder-v01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/plaguss/stablelm-2-1_6-sft-disticoder-v01

SGLang

How to use plaguss/stablelm-2-1_6-sft-disticoder-v01 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 "plaguss/stablelm-2-1_6-sft-disticoder-v01" \
    --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": "plaguss/stablelm-2-1_6-sft-disticoder-v01",
		"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 "plaguss/stablelm-2-1_6-sft-disticoder-v01" \
        --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": "plaguss/stablelm-2-1_6-sft-disticoder-v01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use plaguss/stablelm-2-1_6-sft-disticoder-v01 with Docker Model Runner:
```
docker model run hf.co/plaguss/stablelm-2-1_6-sft-disticoder-v01
```

stablelm-2-1.6-disticoder-v0.1

This model is a fine-tuned version of stabilityai/stablelm-2-1_6b on the argilla/DistiCoder-dpo-binarized dataset. It achieves the following results on the evaluation set:

Loss: 1.1315

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.7319	0.44	5	1.5441
1.3425	0.89	10	1.2968
1.1709	1.33	15	1.2151
1.0994	1.78	20	1.1605
1.0287	2.22	25	1.1382
1.0303	2.67	30	1.1315

Framework versions

Transformers 4.37.2
Pytorch 2.1.1+cu121
Datasets 2.16.1
Tokenizers 0.15.2

Downloads last month: 10

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for plaguss/stablelm-2-1_6-sft-disticoder-v01

Base model

stabilityai/stablelm-2-1_6b

Finetuned

(6)

this model

Adapters

1 model