Instructions to use Vipitis/santacoder-finetuned-the-stack-glsl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vipitis/santacoder-finetuned-the-stack-glsl with Transformers:

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

pipe = pipeline("text-generation", model="Vipitis/santacoder-finetuned-the-stack-glsl", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vipitis/santacoder-finetuned-the-stack-glsl", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Vipitis/santacoder-finetuned-the-stack-glsl", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vipitis/santacoder-finetuned-the-stack-glsl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vipitis/santacoder-finetuned-the-stack-glsl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vipitis/santacoder-finetuned-the-stack-glsl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vipitis/santacoder-finetuned-the-stack-glsl

SGLang

How to use Vipitis/santacoder-finetuned-the-stack-glsl 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 "Vipitis/santacoder-finetuned-the-stack-glsl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vipitis/santacoder-finetuned-the-stack-glsl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Vipitis/santacoder-finetuned-the-stack-glsl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vipitis/santacoder-finetuned-the-stack-glsl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vipitis/santacoder-finetuned-the-stack-glsl with Docker Model Runner:
```
docker model run hf.co/Vipitis/santacoder-finetuned-the-stack-glsl
```

Vipitis commited on Nov 7, 2023

Commit

1aeea42

1 Parent(s): fb98268

Revert all changes

Browse files

back to where we started. might be more complicated than hoped :/

Files changed (1) hide show

config.json +10 -7

config.json CHANGED Viewed

@@ -1,18 +1,21 @@
 {
-  "_name_or_path": "bigcode/santacoder",
-  "activation_function": "gelu_pytorch_tanh",
   "architectures": [
-    "GPTBigCodeForCausalLM"
   ],
-  "attention_softmax_in_fp32": false,
-  "multi_query": true,
   "attn_pdrop": 0.1,
   "bos_token_id": 49152,
   "embd_pdrop": 0.1,
   "eos_token_id": 49152,
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
-  "model_type": "gpt_bigcode",
   "n_embd": 2048,
   "n_head": 16,
   "n_inner": 8192,
@@ -20,7 +23,7 @@
   "n_positions": 2048,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
-  "scale_attention_softmax_in_fp32": false,
   "scale_attn_weights": true,
   "summary_activation": null,
   "summary_first_dropout": 0.1,

 {
+   "_name_or_path": "bigcode/santacoder",
+  "activation_function": "gelu_fast",
   "architectures": [
+    "GPT2LMHeadCustomModel"
   ],
+  "attention_head_type": "multiquery",
   "attn_pdrop": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_gpt2_mq.GPT2CustomConfig",
+    "AutoModelForCausalLM": "modeling_gpt2_mq.GPT2LMHeadCustomModel"
+  },
   "bos_token_id": 49152,
   "embd_pdrop": 0.1,
   "eos_token_id": 49152,
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
   "n_embd": 2048,
   "n_head": 16,
   "n_inner": 8192,
   "n_positions": 2048,
   "reorder_and_upcast_attn": false,
   "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
   "scale_attn_weights": true,
   "summary_activation": null,
   "summary_first_dropout": 0.1,