Instructions to use Kukedlc/NeuralExperiment-7b-MagicCoder-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v6 with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/NeuralExperiment-7b-MagicCoder-v6")

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

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v6")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v6", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kukedlc/NeuralExperiment-7b-MagicCoder-v6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/NeuralExperiment-7b-MagicCoder-v6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v6

SGLang

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v6 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 "Kukedlc/NeuralExperiment-7b-MagicCoder-v6" \
    --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": "Kukedlc/NeuralExperiment-7b-MagicCoder-v6",
		"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 "Kukedlc/NeuralExperiment-7b-MagicCoder-v6" \
        --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": "Kukedlc/NeuralExperiment-7b-MagicCoder-v6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v6 with Docker Model Runner:
```
docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v6
```

Kukedlc commited on Mar 5, 2024

Commit

ff034cd

verified ·

1 Parent(s): 0176c9d

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -38,6 +38,9 @@ Each dataset contributed 20,000 data points to the training process, ensuring a
 !pip install -qU transformers accelerate
 from transformers import AutoTokenizer
 import transformers
 import torch
@@ -56,3 +59,4 @@ pipeline = transformers.pipeline(
 outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 print(outputs[0]["generated_text"])

 !pip install -qU transformers accelerate
+```python
+!pip install -qU transformers accelerate
 from transformers import AutoTokenizer
 import transformers
 import torch
 outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 print(outputs[0]["generated_text"])
+```