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 20, 2023

Commit

2777e2e

1 Parent(s): 1d15734

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@@ -68,8 +68,8 @@ This is an [OpenLlama model Code Instruct](https://huggingface.co/mwitiderrick/o
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
-tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
-model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
 query = "Write a quick sort algorithm in Python"
 text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
 output = text_gen(f"<s>[INST]{query}[/INST]")

 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
+tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_v0.1")
+model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_v0.1")
 query = "Write a quick sort algorithm in Python"
 text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
 output = text_gen(f"<s>[INST]{query}[/INST]")