Instructions to use SurgeGlobal/OpenBezoar-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SurgeGlobal/OpenBezoar-SFT with Transformers:

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

pipe = pipeline("text-generation", model="SurgeGlobal/OpenBezoar-SFT", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SurgeGlobal/OpenBezoar-SFT")
model = AutoModelForCausalLM.from_pretrained("SurgeGlobal/OpenBezoar-SFT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SurgeGlobal/OpenBezoar-SFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SurgeGlobal/OpenBezoar-SFT

SGLang

How to use SurgeGlobal/OpenBezoar-SFT 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 "SurgeGlobal/OpenBezoar-SFT" \
    --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": "SurgeGlobal/OpenBezoar-SFT",
		"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 "SurgeGlobal/OpenBezoar-SFT" \
        --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": "SurgeGlobal/OpenBezoar-SFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SurgeGlobal/OpenBezoar-SFT with Docker Model Runner:
```
docker model run hf.co/SurgeGlobal/OpenBezoar-SFT
```

sachithgunasekara commited on Feb 15, 2025

Commit

ffbea97

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@@ -6,6 +6,8 @@ datasets:
 - SurgeGlobal/Evol-Instruct
 language:
 - en
 ---
 # OpenBezoar-SFT
 The OpenBezoar-SFT is an instruction-tuned version of [Open LlaMA 3B v2](https://huggingface.co/openlm-research/open_llama_3b_v2) with Q-LoRA on three of our custom datasets synthetically generated from [h2ogpt-gm-oasst1-en-2048-falcon-40b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2).

 - SurgeGlobal/Evol-Instruct
 language:
 - en
+base_model:
+- openlm-research/open_llama_3b_v2
 ---
 # OpenBezoar-SFT
 The OpenBezoar-SFT is an instruction-tuned version of [Open LlaMA 3B v2](https://huggingface.co/openlm-research/open_llama_3b_v2) with Q-LoRA on three of our custom datasets synthetically generated from [h2ogpt-gm-oasst1-en-2048-falcon-40b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2).