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

33efe92

verified ·

1 Parent(s): e386947

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -0

README.md CHANGED Viewed

@@ -43,6 +43,17 @@ tags:
 |Avg          |  | 72.0       |    69.2     | 63.6      | 57.4               |
 |     |        |  |      |      |      |      |
 | Model Name         | ARC      | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K    |
 | ------------------ | -------- | --------- | ---- | ---------- | ---------- | -------- |

 |Avg          |  | 72.0       |    69.2     | 63.6      | 57.4               |
 |     |        |  |      |      |      |      |
+|     |        |      |      |      |      |       |
+| --- | ------ |----- |----- |----- |----- |------ |
+|Dataset       | meditron-7b | llama-2-7b  | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* | MT7Bi |
+|MMLU-Medical  | 54.2        |    53.7     |    56.4      |      63.3       |        60.0          | 46.9  |
+|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 |
+|              |             |             |              |                 |                      |  |
 | Model Name         | ARC      | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K    |
 | ------------------ | -------- | --------- | ---- | ---------- | ---------- | -------- |