Instructions to use BhavyaMuni/model-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BhavyaMuni/model-generator with Transformers:

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

pipe = pipeline("text-generation", model="BhavyaMuni/model-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BhavyaMuni/model-generator")
model = AutoModelForCausalLM.from_pretrained("BhavyaMuni/model-generator")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BhavyaMuni/model-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BhavyaMuni/model-generator

SGLang

How to use BhavyaMuni/model-generator 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 "BhavyaMuni/model-generator" \
    --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": "BhavyaMuni/model-generator",
		"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 "BhavyaMuni/model-generator" \
        --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": "BhavyaMuni/model-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BhavyaMuni/model-generator with Docker Model Runner:
```
docker model run hf.co/BhavyaMuni/model-generator
```

BhavyaMuni commited on May 20, 2023

Commit

a638e07

1 Parent(s): 5f5c2a9

trained

Browse files

Files changed (3) hide show

generation_config.json +6 -0
pytorch_model.bin +1 -1
training_args.bin +1 -1

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.29.2"
+}

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c494dd46aa7b4ef66603d80380d6080e30ece35266414b23d5102c371fafd33c
 size 333969394

 version https://git-lfs.github.com/spec/v1
+oid sha256:85991073e81ed07e2826f5bd9adf60c3298bbc86dbdf0cb9408494b860d73a19
 size 333969394

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d7d60ce5b8aff56dd64a3dd6e5d663e78d717947349b9894e5a966c571554965
 size 4404

 version https://git-lfs.github.com/spec/v1
+oid sha256:001fd8a499114814e0be7a402582b2984db4e972c31ac6cf5c19900c7aa3549a
 size 4404