Instructions to use abideen/starcoder2-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abideen/starcoder2-chat with Transformers:

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

pipe = pipeline("text-generation", model="abideen/starcoder2-chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abideen/starcoder2-chat")
model = AutoModelForCausalLM.from_pretrained("abideen/starcoder2-chat")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abideen/starcoder2-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abideen/starcoder2-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/starcoder2-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abideen/starcoder2-chat

SGLang

How to use abideen/starcoder2-chat 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 "abideen/starcoder2-chat" \
    --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": "abideen/starcoder2-chat",
		"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 "abideen/starcoder2-chat" \
        --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": "abideen/starcoder2-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abideen/starcoder2-chat with Docker Model Runner:
```
docker model run hf.co/abideen/starcoder2-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Starcoder-2-chat

Starcoder-2-chat is an instruction fine-tuned of bigcode/starcoder2-7b using the glaiveai/glaive-code-assistant-v2 dataset using LoRA.

🏆 Evaluation results

Thanks to Muhammad Bin Usman for running evals on Starcoder2-chat.

HUMANEVAL

0.3231707317073171

HUMANEVALPLUS

0.25609756097560976

INSTRUCT-HUMANEVAL

0.3231707317073171

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-7
train_batch_size: 2
eval_batch_size: Not specified
seed: Not specified
gradient_accumulation_steps: 8
total_train_batch_size: Not specified
optimizer: PagedAdamW with 32-bit precision
lr_scheduler_type: Cosine
lr_scheduler_warmup_steps: 100
training_epoch: 1

Framework versions

Transformers 4.39.0.dev0
Peft 0.9.1.dev0
Datasets 2.18.0
torch 2.2.0
accelerate 0.27.2

Downloads last month: 15

Safetensors

Model size

7B params

Tensor type

F32

Model tree for abideen/starcoder2-chat

Base model

bigcode/starcoder2-7b

Finetuned

(38)

this model

Quantizations

1 model

Collection including abideen/starcoder2-chat

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