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

e386947

verified ·

1 Parent(s): 3b0be67

Update README.md

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@@ -21,11 +21,34 @@ tags:
 ### Model Evaluation Benchmark
-| Model Name         | ARC   | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
-| ------------------ | ----- | --------- | ---- | ---------- | ---------- | ----- |
-| Orca-2-7b              | **78.4**  | 76.1      | 53.7 | **52.4**       | **74.2**       | **47.2**  |
-| LLAMA-2-7b           | 43.2  | **77.1**      | 44.4 | 38.7       | 69.5       | 16    |
-| MT7Bi (1 epoch)        | 50.94  | 73.24     | -  | 43.04      | 72.06      | 22.52  |
 ### ARC: 50.94%
 |    Task     |Version|       Metric       |    Value    |   |Stderr|

 ### Model Evaluation Benchmark
+|          |        |      |      |      |      |      |      |       |
+| -------- | ------ |----- |----- |----- |----- |----- |----- |------ |
+|Category  | MT7Bi  | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b |
+|Health    |  |      81.8    |    69.1     |    83.6    | 27.3        |   16.4     |      3.6     |
+|Nutrition |  |      77.9    |    68.8     |    62.5    | 31.1        |   12.5     |      6.3     |
+|Psychology|  |      47.4    |    36.8     |    52.6    | 21.1        |   10.5     |      0.0     |
+|Science   |  |      77.8    |    44.4     |    33.3    | 33.3        |   11.1     |      0.0     |
+|Avg       |  |      71.2    |    54.8     |    58.0    | 28.3        |   12.6     |      2.5     |
+|          |  |              |             |            |             |            |              |
+|     |        |        |      |      |      |      |
+| --- | ------ | ------ |----- |----- |----- |----- |
+|Dataset| MT7Bi |     meditron-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
+|MMLU-Medical | 46.9 | 77.6       |    77.9     | 74.5      | 65.7               |
+|PubMedQA     | 65.2 | 81.6       |    80.0     | 61.2      | 67.0               |
+|MedMCQA      | 42.7 | 66.0       |    62.6     | 59.2      | 46.7               |
+|MedQA        |  | 64.4       |    61.5     | 59.1      | 50.8               |
+|MedQA-4-Option| 44.3 | 70.2      |    63.8     | 63.9      | 56.8               |
+|Avg          |  | 72.0       |    69.2     | 63.6      | 57.4               |
+|     |        |  |      |      |      |      |
+| Model Name         | ARC      | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K    |
+| ------------------ | -------- | --------- | ---- | ---------- | ---------- | -------- |
+| Orca-2-7b          | **78.4** | 76.1      | 53.7 | **52.4**   | **74.2**   | **47.2** |
+| LLAMA-2-7b         | 43.2     | **77.1**  | 44.4 | 38.7       | 69.5       | 16       |
+| MT7Bi (1 epoch)    | 50.94    | 73.24     | -    | 43.04      | 72.06      | 22.52    |
 ### ARC: 50.94%
 |    Task     |Version|       Metric       |    Value    |   |Stderr|