Instructions to use bespokelabs/Bespoke-Stratos-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bespokelabs/Bespoke-Stratos-32B with Transformers:

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

pipe = pipeline("text-generation", model="bespokelabs/Bespoke-Stratos-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bespokelabs/Bespoke-Stratos-32B")
model = AutoModelForCausalLM.from_pretrained("bespokelabs/Bespoke-Stratos-32B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bespokelabs/Bespoke-Stratos-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bespokelabs/Bespoke-Stratos-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bespokelabs/Bespoke-Stratos-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bespokelabs/Bespoke-Stratos-32B

SGLang

How to use bespokelabs/Bespoke-Stratos-32B 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 "bespokelabs/Bespoke-Stratos-32B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bespokelabs/Bespoke-Stratos-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bespokelabs/Bespoke-Stratos-32B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bespokelabs/Bespoke-Stratos-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bespokelabs/Bespoke-Stratos-32B with Docker Model Runner:
```
docker model run hf.co/bespokelabs/Bespoke-Stratos-32B
```

i want to reproduce the result, but encounter some inconsistency with your training curve

by sqatwork - opened Feb 6, 2025

Discussion

sqatwork

Feb 6, 2025

i load qwen2.5-32b-instruct model, and use this dataset settings to train
"Sky-T1": {
"hf_hub_url": "NovaSky-AI/Sky-T1_data_17k",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"system": "system"
},
"tags": {
"role_tag": "from",
"content_tag": "value",
"user_tag": "user",
"assistant_tag": "assistant"
}
},
other training settings are exactly the same as you reported. But my training loss starts around 0.5, the training curves are significantly different from you reported. Here is my wandb log : https://wandb.ai/shuqiatwork-minimax/huggingface?nw=nwusershuqiatwork.
Can you help me identify the problem, thanks a lot

sqatwork changed discussion status to closed Feb 6, 2025

ryanmarten

Bespoke Labs org Feb 7, 2025

Are you comparing to the loss curve here?
https://huggingface.co/bespokelabs/Bespoke-Stratos-32B/blob/main/training_loss.png

It looks like you have closed the issue, so I'm going to assume that you resolved it :)

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