Instructions to use mrm8488/santacoder-finetuned-the-stack-bash-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 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-bash-4"
# 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-bash-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-4

SGLang

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 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-bash-4" \
    --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-bash-4",
		"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-bash-4" \
        --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-bash-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/santacoder-finetuned-the-stack-bash-4 with Docker Model Runner:
```
docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-4
```

mrm8488 commited on Jan 23, 2023

Commit

96d581e

1 Parent(s): e84a854

End of training

Browse files

Files changed (2) hide show

README.md +68 -0
runs/Jan22_22-13-10_2c98fbadac25/events.out.tfevents.1674425618.2c98fbadac25.2122.0 +2 -2

README.md ADDED Viewed

	@@ -0,0 +1,68 @@

+---
+license: openrail
+tags:
+- generated_from_trainer
+model-index:
+- name: santacoder-finetuned-the-stack-bash-4
+  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-bash-4
+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: 1.3111
+## 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: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- training_steps: 5000
+### Training results
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.8682        | 0.1   | 500  | 1.5485          |
+| 1.4501        | 0.2   | 1000 | 1.5183          |
+| 1.659         | 0.3   | 1500 | 1.4865          |
+| 1.3649        | 0.4   | 2000 | 1.4528          |
+| 1.4063        | 0.5   | 2500 | 1.4052          |
+| 5.1385        | 0.6   | 3000 | 1.3810          |
+| 1.276         | 0.7   | 3500 | 1.3462          |
+| 1.5288        | 0.8   | 4000 | 1.3222          |
+| 2.0152        | 0.9   | 4500 | 1.3119          |
+| 1.5725        | 1.0   | 5000 | 1.3111          |
+### Framework versions
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2

runs/Jan22_22-13-10_2c98fbadac25/events.out.tfevents.1674425618.2c98fbadac25.2122.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:db91780f35293e21a66474ae251654ce4dc411262bd3918ebda3bc0cfd746a2e
-size 85305

 version https://git-lfs.github.com/spec/v1
+oid sha256:3e1dd7345902b252470b5b7f9f5a74e4bc2df873c9871845aea916bde85a1e70
+size 85659