Instructions to use abjbpi/DS_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abjbpi/DS_small with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abjbpi/DS_small")
model = AutoModelForCausalLM.from_pretrained("abjbpi/DS_small")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abjbpi/DS_small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abjbpi/DS_small

SGLang

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

Docker Model Runner
How to use abjbpi/DS_small with Docker Model Runner:
```
docker model run hf.co/abjbpi/DS_small
```

Abhijnan Bajpai commited on Jun 4, 2021

Commit

a5a85e8

1 Parent(s): 071ef97

add model

Browse files

Files changed (2) hide show

config.json +4 -4
pytorch_model.bin +2 -2

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "output-small",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
@@ -13,10 +13,10 @@
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
-  "n_embd": 768,
-  "n_head": 12,
   "n_inner": null,
-  "n_layer": 12,
   "n_positions": 1024,
   "resid_pdrop": 0.1,
   "scale_attn_weights": true,

 {
+  "_name_or_path": "output-medium",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
+  "n_embd": 1024,
+  "n_head": 16,
   "n_inner": null,
+  "n_layer": 24,
   "n_positions": 1024,
   "resid_pdrop": 0.1,
   "scale_attn_weights": true,

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:538dd74499d948c2359c80d22a318b86e65839115a6eb3bc57a7c760a9b305bf
-size 510405883

 version https://git-lfs.github.com/spec/v1
+oid sha256:e928aaa84057dcf86a333e04386a0a3d5a3f2a42cd29a2abefb40cb22396f921
+size 1444584739