Instructions to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="mwitiderrick/open_llama_3b_glaive_code_v0.1")

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

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mwitiderrick/open_llama_3b_glaive_code_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1

SGLang

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
    --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
        --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Docker Model Runner:
```
docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1
```

mwitiderrick commited on Dec 19, 2023

Commit

00ec376

1 Parent(s): becaf1f

Update README.md

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@@ -28,7 +28,7 @@ model-index:
         metrics:
           - name: hellaswag(0-Shot)
             type: hellaswag (0-Shot)
-            value: 0.
       - task:
           type: text-generation
         dataset:
@@ -37,7 +37,7 @@ model-index:
         metrics:
           - name: winogrande(0-Shot)
             type: winogrande (0-Shot)
-            value: 0.
       - task:
           type: text-generation

         metrics:
           - name: hellaswag(0-Shot)
             type: hellaswag (0-Shot)
+            value: 0.6600
       - task:
           type: text-generation
         dataset:
         metrics:
           - name: winogrande(0-Shot)
             type: winogrande (0-Shot)
+            value: 0.6322
       - task:
           type: text-generation