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 17, 2024

Commit

81be9eb

verified ·

1 Parent(s): b3b11de

Upload folder using huggingface_hub

Browse files

Files changed (3) hide show

pytorch_model-00001-of-00003.bin +1 -1
pytorch_model-00002-of-00003.bin +1 -1
pytorch_model-00003-of-00003.bin +1 -1

pytorch_model-00001-of-00003.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ddf0895d0efcb99809515093794e8f2006a2eb220e4d293274432af3f5acbb01
 size 4939149362

 version https://git-lfs.github.com/spec/v1
+oid sha256:fc9b70dc0a4162670b04bafde40ef83b97d1038c17c6d2095ac7d97a1d48d43a
 size 4939149362

pytorch_model-00002-of-00003.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:15ee09ed7f5b88c4fac49e4eb9df6ddb84ef71ed0c4e64ab11dee29230d449f5
 size 4947416538

 version https://git-lfs.github.com/spec/v1
+oid sha256:5a83c5ed744d67e2ceb0623ab02cb78ef8324863812bec76ddd0fd31a87fe0aa
 size 4947416538

pytorch_model-00003-of-00003.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7c89663575795ff4438cfb28769b9a96295a66de26447a95575c150f25770d30
 size 3590646009

 version https://git-lfs.github.com/spec/v1
+oid sha256:7ccfd4cd5d3e53b8bb0a4a9cafd0e6bbe9bbab53406b1369cafd6d0bb130f86d
 size 3590646009