Instructions to use Technoculture/MT7Bi-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Technoculture/MT7Bi-sft with Transformers:

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

pipe = pipeline("text-generation", model="Technoculture/MT7Bi-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Technoculture/MT7Bi-sft")
model = AutoModelForCausalLM.from_pretrained("Technoculture/MT7Bi-sft")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Technoculture/MT7Bi-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Technoculture/MT7Bi-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Technoculture/MT7Bi-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Technoculture/MT7Bi-sft

SGLang

How to use Technoculture/MT7Bi-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 "Technoculture/MT7Bi-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": "Technoculture/MT7Bi-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 "Technoculture/MT7Bi-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": "Technoculture/MT7Bi-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Technoculture/MT7Bi-sft with Docker Model Runner:
```
docker model run hf.co/Technoculture/MT7Bi-sft
```

satyamt commited on Jan 11, 2024

Commit

c9af653

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1 Parent(s): 33efe92

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@@ -50,8 +50,8 @@ tags:
 |PubMedQA      | 74.4        |    61.8     |    59.2      |      46.0       |        17.8          | 65.2  |
 |MedMCQA       | 59.2        |    54.4     |    57.6      |      43.0       |        40.2          | 42.7  |
 |MedQA         | 47.9        |    44.0     |    42.4      |      42.8       |        32.4          |  |
-|MedQA-4-Option| 52.0        |    49.6     |    49.2      |      48.5       |        41.1          |  |
-|Avg           | 57.5        |    52.7     |    53.0      |      48.7       |        38.3          | 44.3 |
 |              |             |             |              |                 |                      |  |

 |PubMedQA      | 74.4        |    61.8     |    59.2      |      46.0       |        17.8          | 65.2  |
 |MedMCQA       | 59.2        |    54.4     |    57.6      |      43.0       |        40.2          | 42.7  |
 |MedQA         | 47.9        |    44.0     |    42.4      |      42.8       |        32.4          |  |
+|MedQA-4-Option| 52.0        |    49.6     |    49.2      |      48.5       |        41.1          | 44.3 |
+|Avg           | 57.5        |    52.7     |    53.0      |      48.7       |        38.3          |  |
 |              |             |             |              |                 |                      |  |