Instructions to use Mxode/Pythia-70m-C-Language-KnowledgeExtract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mxode/Pythia-70m-C-Language-KnowledgeExtract with Transformers:

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

pipe = pipeline("text-generation", model="Mxode/Pythia-70m-C-Language-KnowledgeExtract")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mxode/Pythia-70m-C-Language-KnowledgeExtract")
model = AutoModelForCausalLM.from_pretrained("Mxode/Pythia-70m-C-Language-KnowledgeExtract")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mxode/Pythia-70m-C-Language-KnowledgeExtract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mxode/Pythia-70m-C-Language-KnowledgeExtract"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mxode/Pythia-70m-C-Language-KnowledgeExtract",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mxode/Pythia-70m-C-Language-KnowledgeExtract

SGLang

How to use Mxode/Pythia-70m-C-Language-KnowledgeExtract 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 "Mxode/Pythia-70m-C-Language-KnowledgeExtract" \
    --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": "Mxode/Pythia-70m-C-Language-KnowledgeExtract",
		"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 "Mxode/Pythia-70m-C-Language-KnowledgeExtract" \
        --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": "Mxode/Pythia-70m-C-Language-KnowledgeExtract",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mxode/Pythia-70m-C-Language-KnowledgeExtract with Docker Model Runner:
```
docker model run hf.co/Mxode/Pythia-70m-C-Language-KnowledgeExtract
```

Mxode commited on Oct 6, 2023

Commit

139a5db

1 Parent(s): 71fb641

Update README.md

Browse files

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README.md +12 -1

README.md CHANGED Viewed

@@ -7,11 +7,20 @@ tags:
 - knowledge extraction
 - tiny
 - small
 ---
 A model that can **extract the knowledge points** from the given **C language code**.
 The base model is [pythia-70m](https://huggingface.co/EleutherAI/pythia-70m). This model was fine-tuned with 10 epochs using [Q-Lora](https://github.com/artidoro/qlora) method on my own training set.
 A usage example is as follows, first import the model and prepare the code:
 ```python
@@ -66,6 +75,8 @@ response = tokenizer.decode(tokens[0]).split('```')[-1].split('<')[0]
 However, in practical use, in order to achieve more diverse representations, it's recommended to do multiple inferences. Don't worry, it's really small so the inferences don't take much time, as follows:
 ```python
@@ -95,5 +106,5 @@ print(ans_dict)
 ###     'Quick sort': 25,
 ###     'Recurrence': 2,
 ###     'Queue': 1
-###}
 ```

 - knowledge extraction
 - tiny
 - small
+- C
 ---
+## Model info
 A model that can **extract the knowledge points** from the given **C language code**.
 The base model is [pythia-70m](https://huggingface.co/EleutherAI/pythia-70m). This model was fine-tuned with 10 epochs using [Q-Lora](https://github.com/artidoro/qlora) method on my own training set.
+## How to use
+### quick start
 A usage example is as follows, first import the model and prepare the code:
 ```python
+### and more
 However, in practical use, in order to achieve more diverse representations, it's recommended to do multiple inferences. Don't worry, it's really small so the inferences don't take much time, as follows:
 ```python
 ###     'Quick sort': 25,
 ###     'Recurrence': 2,
 ###     'Queue': 1
+### }
 ```