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", device_map="auto")

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
```

Afeen commited on Jan 11, 2024

Commit

892f072

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1 Parent(s): 9eee4fe

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@@ -21,7 +21,7 @@ tags:
 | ------------------ | ----- | --------- | ---- | ---------- | ---------- | ----- |
 | Orca2              | 78.4  | 76.1      | 53.7 | 52.4       | 74.2       | 47.2  |
 | LLAMA-7b           | 43.2  | 77.1      | 44.4 | 38.7       | 69.5       | 16    |
-| MMD7b-alpha        | 50.9  | 73.24     | N/A  | 43.04      | 72.06      | 22.52  |
 ## Open LLM Leaderboard

 | ------------------ | ----- | --------- | ---- | ---------- | ---------- | ----- |
 | Orca2              | 78.4  | 76.1      | 53.7 | 52.4       | 74.2       | 47.2  |
 | LLAMA-7b           | 43.2  | 77.1      | 44.4 | 38.7       | 69.5       | 16    |
+| MMD7b-alpha        | 50.94  | 73.24     | N/A  | 43.04      | 72.06      | 22.52  |
 ## Open LLM Leaderboard