Instructions to use mrm8488/santacoder-finetuned-the-stack-rust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/santacoder-finetuned-the-stack-rust with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/santacoder-finetuned-the-stack-rust", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-rust", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-rust", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrm8488/santacoder-finetuned-the-stack-rust with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/santacoder-finetuned-the-stack-rust"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/santacoder-finetuned-the-stack-rust",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-rust

SGLang

How to use mrm8488/santacoder-finetuned-the-stack-rust 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 "mrm8488/santacoder-finetuned-the-stack-rust" \
    --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": "mrm8488/santacoder-finetuned-the-stack-rust",
		"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 "mrm8488/santacoder-finetuned-the-stack-rust" \
        --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": "mrm8488/santacoder-finetuned-the-stack-rust",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/santacoder-finetuned-the-stack-rust with Docker Model Runner:
```
docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-rust
```

santacoder-finetuned-the-stack-rust / README.md

mrm8488

End of training

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	---
	license: openrail
	tags:
	- generated_from_trainer
	model-index:
	- name: santacoder-finetuned-the-stack-rust
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# santacoder-finetuned-the-stack-rust

	This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7999

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- training_steps: 10000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|
	\| 1.2075 \| 0.05 \| 500 \| 1.0610 \|
	\| 1.79 \| 0.1 \| 1000 \| 1.0754 \|
	\| 1.2441 \| 0.15 \| 1500 \| 1.0339 \|
	\| 1.1709 \| 0.2 \| 2000 \| 0.9829 \|
	\| 0.7645 \| 0.25 \| 2500 \| 0.9738 \|
	\| 1.0381 \| 0.3 \| 3000 \| 0.9536 \|
	\| 1.0625 \| 0.35 \| 3500 \| 0.9268 \|
	\| 0.78 \| 0.4 \| 4000 \| 0.9130 \|
	\| 0.9294 \| 0.45 \| 4500 \| 0.9001 \|
	\| 0.9767 \| 0.5 \| 5000 \| 0.8857 \|
	\| 5.7027 \| 0.55 \| 5500 \| 0.8728 \|
	\| 0.9476 \| 0.6 \| 6000 \| 0.8556 \|
	\| 0.6185 \| 0.65 \| 6500 \| 0.8404 \|
	\| 0.5057 \| 0.7 \| 7000 \| 0.8328 \|
	\| 0.6451 \| 0.75 \| 7500 \| 0.8199 \|
	\| 0.8298 \| 0.8 \| 8000 \| 0.8111 \|
	\| 0.2447 \| 0.85 \| 8500 \| 0.8069 \|
	\| 0.8177 \| 0.9 \| 9000 \| 0.8020 \|
	\| 0.7184 \| 0.95 \| 9500 \| 0.8003 \|
	\| 0.9166 \| 1.0 \| 10000 \| 0.7999 \|


	### Framework versions

	- Transformers 4.26.0.dev0
	- Pytorch 1.13.1+cu116
	- Datasets 2.7.1
	- Tokenizers 0.13.2