Instructions to use TheBloke/starcoderplus-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/starcoderplus-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/starcoderplus-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/starcoderplus-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/starcoderplus-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TheBloke/starcoderplus-GPTQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheBloke/starcoderplus-GPTQ

SGLang

How to use TheBloke/starcoderplus-GPTQ 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 "TheBloke/starcoderplus-GPTQ" \
    --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": "TheBloke/starcoderplus-GPTQ",
		"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 "TheBloke/starcoderplus-GPTQ" \
        --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": "TheBloke/starcoderplus-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/starcoderplus-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/starcoderplus-GPTQ
```

TheBloke commited on Jun 9, 2023

Commit

aa67ff4

1 Parent(s): 1aa0491

Update GPTQ model, quantised with sequence_length=8192

Browse files

Files changed (2) hide show

config.json +38 -38
gptq_model-4bit--1g.safetensors +1 -1

config.json CHANGED Viewed

@@ -1,39 +1,39 @@
 {
-    "_name_or_path": "/fsx/bigcode/experiments/pretraining/conversions/starcoderplus/large-model",
-    "activation_function": "gelu",
-    "architectures": [
-        "GPTBigCodeForCausalLM"
-    ],
-    "attention_softmax_in_fp32": true,
-    "multi_query": true,
-    "attn_pdrop": 0.1,
-    "bos_token_id": 0,
-    "embd_pdrop": 0.1,
-    "eos_token_id": 0,
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-    "initializer_range": 0.02,
-    "layer_norm_epsilon": 1e-05,
-    "max_batch_size": null,
-    "max_sequence_length": null,
-    "model_type": "gpt_bigcode",
-    "n_embd": 6144,
-    "n_head": 48,
-    "n_inner": 24576,
-    "n_layer": 40,
-    "n_positions": 8192,
-    "pad_key_length": true,
-    "pre_allocate_kv_cache": false,
-    "resid_pdrop": 0.1,
-    "scale_attention_softmax_in_fp32": true,
-    "scale_attn_weights": true,
-    "summary_activation": null,
-    "summary_first_dropout": 0.1,
-    "summary_proj_to_labels": true,
-    "summary_type": "cls_index",
-    "summary_use_proj": true,
-    "torch_dtype": "float32",
-    "transformers_version": "4.28.1",
-    "use_cache": true,
-    "validate_runner_input": true,
-    "vocab_size": 49152
-}

 {
+  "_name_or_path": "/workspace/process/starcoderplus/source",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPTBigCodeForCausalLM"
+  ],
+  "attention_softmax_in_fp32": true,
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 0,
+  "inference_runner": 0,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_batch_size": null,
+  "max_sequence_length": null,
+  "model_type": "gpt_bigcode",
+  "multi_query": true,
+  "n_embd": 6144,
+  "n_head": 48,
+  "n_inner": 24576,
+  "n_layer": 40,
+  "n_positions": 8192,
+  "pad_key_length": true,
+  "pre_allocate_kv_cache": false,
+  "resid_pdrop": 0.1,
+  "scale_attention_softmax_in_fp32": true,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.0.dev0",
+  "use_cache": true,
+  "validate_runner_input": true,
+  "vocab_size": 49152
+}

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