Instructions to use wyt2000/InverseCoder-CL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wyt2000/InverseCoder-CL-7B with Transformers:

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

pipe = pipeline("text-generation", model="wyt2000/InverseCoder-CL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wyt2000/InverseCoder-CL-7B")
model = AutoModelForCausalLM.from_pretrained("wyt2000/InverseCoder-CL-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wyt2000/InverseCoder-CL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wyt2000/InverseCoder-CL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wyt2000/InverseCoder-CL-7B

SGLang

How to use wyt2000/InverseCoder-CL-7B 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 "wyt2000/InverseCoder-CL-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "wyt2000/InverseCoder-CL-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/InverseCoder-CL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wyt2000/InverseCoder-CL-7B with Docker Model Runner:
```
docker model run hf.co/wyt2000/InverseCoder-CL-7B
```

wyt2000 commited on Jul 9, 2024

Commit

e103530

verified ·

1 Parent(s): db1b10c

Update README.md

Browse files

Files changed (1) hide show

README.md +32 -1

README.md CHANGED Viewed

@@ -149,6 +149,30 @@ InverseCoder is a series of code LLMs instruction-tuned by generating data from
 | 7B  | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)          | [wyt2000/InverseCoder-CL-7B](https://huggingface.co/wyt2000/InverseCoder-CL-7B)                   | [wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K)       |
 | 6.7B | [deepseek-ai/deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) | [wyt2000/InverseCoder-DS-6.7B](https://huggingface.co/wyt2000/InverseCoder-DS-6.7B)               | [wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K)     |
 ## Paper
 **Arxiv:** <https://arxiv.org/abs/2407.05700>
@@ -164,4 +188,11 @@ Please cite the paper if you use the models or datasets from InverseCoder.
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2407.05700},
 }
-```

 | 7B  | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)          | [wyt2000/InverseCoder-CL-7B](https://huggingface.co/wyt2000/InverseCoder-CL-7B)                   | [wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K)       |
 | 6.7B | [deepseek-ai/deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) | [wyt2000/InverseCoder-DS-6.7B](https://huggingface.co/wyt2000/InverseCoder-DS-6.7B)               | [wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K)     |
+## Usage
+Similar to [Magicoder-S-DS-6.7B](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B/), use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.
+```python
+from transformers import pipeline
+import torch
+INVERSECODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+@@ Instruction
+{instruction}
+@@ Response
+"""
+instruction = <Your code instruction here>
+prompt = INVERSECODER_PROMPT.format(instruction=instruction)
+generator = pipeline(
+    model="wyt2000/InverseCoder-CL-7B",
+    task="text-generation",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
+print(result[0]["generated_text"])
+```
 ## Paper
 **Arxiv:** <https://arxiv.org/abs/2407.05700>
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2407.05700},
 }
+```
+## Acknowledgements
+* [Magicoder](https://github.com/ise-uiuc/magicoder): Training code, original dataset and data decontamination
+* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for InverseCoder-DS
+* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for InverseCoder-CL
+* [AutoMathText](https://github.com/yifanzhang-pro/AutoMathText): Self-evaluation and Data Selection method