Instructions to use abacaj/starcoderbase-1b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacaj/starcoderbase-1b-sft with Transformers:

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

pipe = pipeline("text-generation", model="abacaj/starcoderbase-1b-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abacaj/starcoderbase-1b-sft")
model = AutoModelForCausalLM.from_pretrained("abacaj/starcoderbase-1b-sft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abacaj/starcoderbase-1b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacaj/starcoderbase-1b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacaj/starcoderbase-1b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abacaj/starcoderbase-1b-sft

SGLang

How to use abacaj/starcoderbase-1b-sft 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 "abacaj/starcoderbase-1b-sft" \
    --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": "abacaj/starcoderbase-1b-sft",
		"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 "abacaj/starcoderbase-1b-sft" \
        --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": "abacaj/starcoderbase-1b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abacaj/starcoderbase-1b-sft with Docker Model Runner:
```
docker model run hf.co/abacaj/starcoderbase-1b-sft
```

starcoderbase-1b-sft / README.md

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	---
	datasets:
	- theblackcat102/evol-codealpaca-v1
	model-index:
	- name: abacaj/starcoderbase-1b-sft
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 39
	verified: false
	- task:
	type: text-generation
	dataset:
	type: mbpp
	name: MBPP
	metrics:
	- name: pass@1
	type: pass@1
	value: 31.74
	verified: false
	language:
	- en
	---

	Dataset credits go to: [theblackcat102](https://huggingface.co/theblackcat102)

	How to run inference:
	```python
	import transformers
	import torch


	def fmt_prompt(prompt: str) -> str:
	return f"""[Instructions]:\n{prompt}\n\n[Response]:"""


	if __name__ == "__main__":
	model_name = "abacaj/starcoderbase-1b-sft"
	tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

	model = (
	transformers.AutoModelForCausalLM.from_pretrained(
	model_name,
	)
	.to("cuda:0")
	.eval()
	)

	prompt = "Write a python function to sort the following array in ascending order, don't use any built in sorting methods: [9,2,8,1,5]"
	prompt_input = fmt_prompt(prompt)
	inputs = tokenizer(prompt_input, return_tensors="pt").to(model.device)
	input_ids_cutoff = inputs.input_ids.size(dim=1)

	with torch.no_grad():
	generated_ids = model.generate(
	**inputs,
	use_cache=True,
	max_new_tokens=512,
	temperature=0.2,
	top_p=0.95,
	do_sample=True,
	eos_token_id=tokenizer.eos_token_id,
	pad_token_id=tokenizer.pad_token_id,
	)

	completion = tokenizer.decode(
	generated_ids[0][input_ids_cutoff:],
	skip_special_tokens=True,
	)

	print(completion)
	```

	Evals:
	![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/U7L1aOV7UxBEBcLGqOZ2s.png)

	Training charts:
	![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/PLkFqE7_34-hJmFW7_opG.png)

	Link to charts:
	https://api.wandb.ai/links/abacaj1/c4nkcs9r

	Code to train model:
	https://github.com/abacaj/train-with-fsdp