Instructions to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/NeuralExperiment-7b-MagicCoder-v7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v7")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v7", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 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-v7"
# 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-v7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v7

SGLang

How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 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-v7" \
    --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-v7",
		"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-v7" \
        --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-v7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with Docker Model Runner:
```
docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v7
```

NeuralExperiment-7b-MagicCoder-v7 / README.md

Kukedlc

Update README.md

3ac5143 verified over 2 years ago

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	---
	license: apache-2.0
	datasets:
	- microsoft/orca-math-word-problems-200k
	- ise-uiuc/Magicoder-Evol-Instruct-110K
	- Vezora/Tested-22k-Python-Alpaca
	---

	# Datacard for Custom Trained Model
	- Base Model : [Kukedlc/NeuralExperiment-7b-dare-ties](https://huggingface.co/Kukedlc/NeuralExperiment-7b-dare-ties)


	## Model Description
	This model is an experimental AI trained on three distinct datasets focusing on logical reasoning, mathematics, and programming. The training process involved fine-tuning from the last layer (31) backward with a gradually decreasing learning rate. The primary goal is to address and rectify the common 'INSTINST' bug observed in leaderboard models through targeted training on the latest layers.

	## Datasets Used for Training
	- `microsoft/orca-math-word-problems-200k`: A large-scale dataset of mathematical word problems aimed at enhancing the model's numerical reasoning and problem-solving capabilities.
	- `ise-uiuc/Magicoder-Evol-Instruct-110K`: A dataset designed to improve code generation and understanding, contributing to the model's programming language proficiency.
	- `sahil2801/CodeAlpaca-20k`: A dataset focused on programming challenges to further refine the model's coding and logical reasoning skills.

	Each dataset contributed 20,000 data points to the training process, ensuring a balanced representation of logic, mathematics, and programming tasks.

	## Training Environment
	- The model was trained on Kaggle's free GPU environment, allowing for cost-effective fine-tuning and experimentation.
	- Users interested in replicating or extending this training can find the Kaggle notebook in my profile or request it directly for collaborative purposes.

	## Preliminary Results
	- The model shows promising results in solving logical puzzles and mathematical problems, especially those with misleading or non-obvious solutions that it initially struggled with.
	- Ongoing experiments aim to quantify the impact of targeted training on the model's reasoning capabilities across different domains.

	## Invitation for Collaboration
	- Feedback, suggestions, and collaborative efforts are highly encouraged to further refine and evaluate the model.
	- If interested in contributing or experimenting with this model, please feel free to reach out or access the code directly from my Kaggle profile.

	## Contact Information
	- For any inquiries, suggestions, or collaboration proposals, please contact me!

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "Kukedlc/NeuralExperiment-7b-MagicCoder-v7"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	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"])
	```

	![Kukedlc/NeuralExperiment-7b-dare-ties](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202024-03-05%2000.28.41%20-%20Imagine%20a%20visual%20representation%20of%20a%20language%20model%20inspired%20by%20the%20Mandelbrot%20fractal.%20The%20scene%20should%20depict%20an%20abstract%2C%20intricate%20network%20resembl.webp)