Instructions to use allstax/CodeExplainer-7b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allstax/CodeExplainer-7b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="allstax/CodeExplainer-7b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allstax/CodeExplainer-7b-v0.1")
model = AutoModelForCausalLM.from_pretrained("allstax/CodeExplainer-7b-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allstax/CodeExplainer-7b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allstax/CodeExplainer-7b-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": "allstax/CodeExplainer-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/allstax/CodeExplainer-7b-v0.1

SGLang

How to use allstax/CodeExplainer-7b-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 "allstax/CodeExplainer-7b-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": "allstax/CodeExplainer-7b-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 "allstax/CodeExplainer-7b-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": "allstax/CodeExplainer-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use allstax/CodeExplainer-7b-v0.1 with Docker Model Runner:
```
docker model run hf.co/allstax/CodeExplainer-7b-v0.1
```

CodeExplainer-7b-v0.1 / README.md

allstax

Update README.md

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	---
	base_model: mistralai/Mistral-7B-v0.1
	license: apache-2.0
	---

	# Code Explainer

	The model does its best to explain python code in plain language


	# Model Details
	Trained by: trained by AllStax Technologies
	Model type: CodeExplainer-7b-v0.1 is a language model based on mistralai/Mistral-7B-v0.1.
	Language(s): English
	We fine-tuned using a data generated by GPT-3.5 and other models.

	# Prompting
	Prompt Template for alpaca style
	```
	### Instruction:

	<prompt>

	### Response:
	```

	# Loading the model
	```
	from transformers import AutoModelForCausalLM, AutoTokenizer
	model_id = "allstax/CodeExplainer-7b-v0.1"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	quant_model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto')
	```