Instructions to use HuggingFaceH4/starchat-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/starchat-alpha with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/starchat-alpha")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/starchat-alpha")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceH4/starchat-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/starchat-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/starchat-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/starchat-alpha

SGLang

How to use HuggingFaceH4/starchat-alpha 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 "HuggingFaceH4/starchat-alpha" \
    --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": "HuggingFaceH4/starchat-alpha",
		"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 "HuggingFaceH4/starchat-alpha" \
        --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": "HuggingFaceH4/starchat-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceH4/starchat-alpha with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/starchat-alpha
```

starchat-alpha / trainer_state.json

lewtun HF Staff

Duplicate from HuggingFaceH4/starcoderbase-finetuned-oasst1

d75a7c3 about 3 years ago

raw

history blame contribute delete

2.55 kB

	{
	"best_metric": null,
	"best_model_checkpoint": null,
	"epoch": 2.9770992366412212,
	"global_step": 195,
	"is_hyper_param_search": false,
	"is_local_process_zero": true,
	"is_world_process_zero": true,
	"log_history": [
	{
	"epoch": 0.02,
	"learning_rate": 0.0,
	"loss": 2.2517,
	"step": 1
	},
	{
	"epoch": 0.24,
	"learning_rate": 2e-05,
	"loss": 1.9184,
	"step": 16
	},
	{
	"epoch": 0.49,
	"learning_rate": 2e-05,
	"loss": 1.7725,
	"step": 32
	},
	{
	"epoch": 0.73,
	"learning_rate": 2e-05,
	"loss": 1.7075,
	"step": 48
	},
	{
	"epoch": 0.98,
	"learning_rate": 2e-05,
	"loss": 1.6668,
	"step": 64
	},
	{
	"epoch": 0.99,
	"eval_loss": 1.6166764497756958,
	"eval_runtime": 22.5473,
	"eval_samples_per_second": 38.985,
	"eval_steps_per_second": 2.439,
	"step": 65
	},
	{
	"epoch": 1.22,
	"learning_rate": 2e-05,
	"loss": 1.4567,
	"step": 80
	},
	{
	"epoch": 1.47,
	"learning_rate": 2e-05,
	"loss": 1.4109,
	"step": 96
	},
	{
	"epoch": 1.71,
	"learning_rate": 2e-05,
	"loss": 1.3875,
	"step": 112
	},
	{
	"epoch": 1.95,
	"learning_rate": 2e-05,
	"loss": 1.3584,
	"step": 128
	},
	{
	"epoch": 2.0,
	"eval_loss": 1.512587547302246,
	"eval_runtime": 22.5311,
	"eval_samples_per_second": 39.013,
	"eval_steps_per_second": 2.441,
	"step": 131
	},
	{
	"epoch": 2.2,
	"learning_rate": 2e-05,
	"loss": 1.1651,
	"step": 144
	},
	{
	"epoch": 2.44,
	"learning_rate": 2e-05,
	"loss": 1.1092,
	"step": 160
	},
	{
	"epoch": 2.69,
	"learning_rate": 2e-05,
	"loss": 1.0948,
	"step": 176
	},
	{
	"epoch": 2.93,
	"learning_rate": 2e-05,
	"loss": 1.0949,
	"step": 192
	},
	{
	"epoch": 2.98,
	"eval_loss": 1.4942539930343628,
	"eval_runtime": 22.489,
	"eval_samples_per_second": 39.086,
	"eval_steps_per_second": 2.446,
	"step": 195
	},
	{
	"epoch": 2.98,
	"step": 195,
	"total_flos": 417490551177216.0,
	"train_loss": 1.4244933299529248,
	"train_runtime": 3089.4842,
	"train_samples_per_second": 8.13,
	"train_steps_per_second": 0.063
	}
	],
	"max_steps": 195,
	"num_train_epochs": 3,
	"total_flos": 417490551177216.0,
	"trial_name": null,
	"trial_params": null
	}