Instructions to use hf-internal-testing/tiny-random-codegen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-codegen with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-codegen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-codegen")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-codegen")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-codegen with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-codegen

SGLang

How to use hf-internal-testing/tiny-random-codegen 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 "hf-internal-testing/tiny-random-codegen" \
    --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": "hf-internal-testing/tiny-random-codegen",
		"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 "hf-internal-testing/tiny-random-codegen" \
        --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": "hf-internal-testing/tiny-random-codegen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-codegen with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-codegen
```

lewtun HF Staff commited on Sep 8, 2022

Commit

a524570

1 Parent(s): cfb7065

Upload CodeGenForCausalLM

Browse files

Files changed (2) hide show

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

config.json CHANGED Viewed

@@ -12,12 +12,12 @@
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
   "model_type": "codegen",
-  "n_ctx": 512,
-  "n_embd": 32,
   "n_head": 4,
   "n_inner": null,
   "n_layer": 5,
-  "n_positions": 512,
   "resid_pdrop": 0.0,
   "rotary_dim": 32,
   "scale_attn_weights": true,
@@ -38,5 +38,5 @@
   "torch_dtype": "float32",
   "transformers_version": "4.22.0.dev0",
   "use_cache": true,
-  "vocab_size": 1000
 }

   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
   "model_type": "codegen",
+  "n_ctx": 2048,
+  "n_embd": 8,
   "n_head": 4,
   "n_inner": null,
   "n_layer": 5,
+  "n_positions": 2048,
   "resid_pdrop": 0.0,
   "rotary_dim": 32,
   "scale_attn_weights": true,
   "torch_dtype": "float32",
   "transformers_version": "4.22.0.dev0",
   "use_cache": true,
+  "vocab_size": 51200
 }

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