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
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+85

-73

Files changed (1) hide show

README.md +85 -73

README.md CHANGED Viewed

@@ -1,74 +1,86 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- llama-factory
-- full
-- generated_from_trainer
-model-index:
-- name: original
-  results: []
-language:
-- en
-datasets:
-- bespokelabs/Bespoke-Stratos-17k
----
-<p align="center">
-    <img src="https://huggingface.co/bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550">
-</p>
-## 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 [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
-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 [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
-It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks:
-||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)|
-|---|---|---|---|---|
-|AIME2024|20.0|10.0|43.3|55.5|
-|MATH500|82.0|74.2|89.4|92.8|
-|GPQA-Diamond|37.8|33.3|44.9|49.1|
-|LiveCodeBench v2 Easy|71.4|65.9|81.3|-|
-|LiveCodeBench v2 Medium|25.5|18.9|42.2|-|
-|LiveCodeBench v2 Hard|1.6|3.3|2.4|-|
-|LiveCodeBench v2 All|36.1|31.9|46.6|-|
-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
-Apache 2.0 License
-## Training procedure
-We used 8xH100 to train the model for 7 hours.
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 12
-- total_train_batch_size: 96
-- total_eval_batch_size: 64
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3.0
-### Training results
-### Framework versions
-- 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
+- full
+- generated_from_trainer
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- bespokelabs/Bespoke-Stratos-17k
+model-index:
+- name: original
+  results: []
+---
+<p align="center">
+    <img src="https://huggingface.co/bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550">
+</p>
+## 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 [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
+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 [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
+It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks:
+||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)|
+|---|---|---|---|---|
+|AIME2024|20.0|10.0|43.3|55.5|
+|MATH500|82.0|74.2|89.4|92.8|
+|GPQA-Diamond|37.8|33.3|44.9|49.1|
+|LiveCodeBench v2 Easy|71.4|65.9|81.3|-|
+|LiveCodeBench v2 Medium|25.5|18.9|42.2|-|
+|LiveCodeBench v2 Hard|1.6|3.3|2.4|-|
+|LiveCodeBench v2 All|36.1|31.9|46.6|-|
+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
+Apache 2.0 License
+## Training procedure
+We used 8xH100 to train the model for 7 hours.
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 12
+- total_train_batch_size: 96
+- total_eval_batch_size: 64
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3.0
+### Training results
+### Framework versions
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
 - Tokenizers 0.20.3