Instructions to use h2oai/h2ogpt-16k-codellama-34b-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h2oai/h2ogpt-16k-codellama-34b-python with Transformers:

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

pipe = pipeline("text-generation", model="h2oai/h2ogpt-16k-codellama-34b-python")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-16k-codellama-34b-python")
model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-16k-codellama-34b-python", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use h2oai/h2ogpt-16k-codellama-34b-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h2oai/h2ogpt-16k-codellama-34b-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h2oai/h2ogpt-16k-codellama-34b-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/h2oai/h2ogpt-16k-codellama-34b-python

SGLang

How to use h2oai/h2ogpt-16k-codellama-34b-python 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 "h2oai/h2ogpt-16k-codellama-34b-python" \
    --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": "h2oai/h2ogpt-16k-codellama-34b-python",
		"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 "h2oai/h2ogpt-16k-codellama-34b-python" \
        --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": "h2oai/h2ogpt-16k-codellama-34b-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use h2oai/h2ogpt-16k-codellama-34b-python with Docker Model Runner:
```
docker model run hf.co/h2oai/h2ogpt-16k-codellama-34b-python
```

arnocandel commited on Aug 25, 2023

Commit

37c8b90

1 Parent(s): c52fe3a

Update config.json

Browse files

Files changed (1) hide show

config.json +2 -2

config.json CHANGED Viewed

@@ -8,7 +8,7 @@
   "hidden_size": 8192,
   "initializer_range": 0.02,
   "intermediate_size": 22016,
-  "max_position_embeddings": 2048,
   "model_type": "llama",
   "num_attention_heads": 64,
   "num_hidden_layers": 48,
@@ -20,5 +20,5 @@
   "torch_dtype": "float16",
   "transformers_version": "4.32.0",
   "use_cache": true,
-  "vocab_size": 32016
 }

   "hidden_size": 8192,
   "initializer_range": 0.02,
   "intermediate_size": 22016,
+  "max_position_embeddings": 16384,
   "model_type": "llama",
   "num_attention_heads": 64,
   "num_hidden_layers": 48,
   "torch_dtype": "float16",
   "transformers_version": "4.32.0",
   "use_cache": true,
+  "vocab_size": 32000
 }