Instructions to use bespokelabs/Bespoke-Stratos-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bespokelabs/Bespoke-Stratos-7B with Transformers:

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

pipe = pipeline("text-generation", model="bespokelabs/Bespoke-Stratos-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bespokelabs/Bespoke-Stratos-7B")
model = AutoModelForCausalLM.from_pretrained("bespokelabs/Bespoke-Stratos-7B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bespokelabs/Bespoke-Stratos-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bespokelabs/Bespoke-Stratos-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bespokelabs/Bespoke-Stratos-7B

SGLang

How to use bespokelabs/Bespoke-Stratos-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bespokelabs/Bespoke-Stratos-7B with Docker Model Runner:
```
docker model run hf.co/bespokelabs/Bespoke-Stratos-7B
```

madiator commited on Jan 22, 2025

Commit

3e7e25c

verified ·

1 Parent(s): 7012d00

Update README

Browse files

Files changed (1) hide show

README.md +18 -14

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 library_name: transformers
-license: other
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - llama-factory
@@ -9,28 +9,32 @@ tags:
 model-index:
 - name: original
   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. -->
-# original
-This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the Stratos-R1 dataset.
 ## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
 ## Training procedure
 ### Training hyperparameters
@@ -58,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.46.1
 - Pytorch 2.5.1+cu124
 - Datasets 3.1.0
-- Tokenizers 0.20.3

 ---
 library_name: transformers
+license: apache-2.0
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - llama-factory
 model-index:
 - name: original
   results: []
+language:
+- en
 ---
 ## Model description
+This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [Stratos-R1 dataset](https://huggingface.co/datasets/bespokelabs/stratos-r1).
+The dataset is derived by distilling DeepSeek-R1 using the data pipeline of Berkeley NovaSky’s Sky-T1 with some modifications. More info in the dataset card at [Stratos-R1 dataset](https://huggingface.co/datasets/bespokelabs/stratos-r1).
+It outperforms Qwen-2.5-7B-Instruct on reasoning benchmarks:
+||Bespoke-Stratos-7B|DeepSeek-R1-Distill-Qwen-7B|Qwen2.5-7B-Instruct|
+|---|---|---|---|
+|AIME2024|20.0|55.5|10.0|
+|MATH500|82.0|83.3|74.2|
+|GPQA-Diamond|37.8|49.1|33.3|
+|LiveCodeBench|-|37.6|32.9|
+Note that the authors of Sky-T1 had [noted](https://github.com/NovaSky-AI/SkyThought/issues/4#issuecomment-2585860004) that they saw little or no improvement in training 7B or 14B models with their data.
+However, see an improvement, though not at the scale of DeepSeek's distilled model. The reason could be that we used 17k examples, while DeepSeek seems to have used 800k.
+## Intended uses & limitations
+Non-commercial use.
 ## Training procedure
+We used 8xH100 to train the model.
 ### Training hyperparameters
 - Transformers 4.46.1
 - Pytorch 2.5.1+cu124
 - Datasets 3.1.0
+- Tokenizers 0.20.3