Instructions to use sahil2801/instruct-codegen-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sahil2801/instruct-codegen-16B with Transformers:

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

pipe = pipeline("text-generation", model="sahil2801/instruct-codegen-16B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sahil2801/instruct-codegen-16B")
model = AutoModelForCausalLM.from_pretrained("sahil2801/instruct-codegen-16B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sahil2801/instruct-codegen-16B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sahil2801/instruct-codegen-16B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sahil2801/instruct-codegen-16B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sahil2801/instruct-codegen-16B

SGLang

How to use sahil2801/instruct-codegen-16B 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 "sahil2801/instruct-codegen-16B" \
    --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": "sahil2801/instruct-codegen-16B",
		"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 "sahil2801/instruct-codegen-16B" \
        --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": "sahil2801/instruct-codegen-16B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sahil2801/instruct-codegen-16B with Docker Model Runner:
```
docker model run hf.co/sahil2801/instruct-codegen-16B
```

sahil2801 commited on May 26, 2023

Commit

652196c

1 Parent(s): c21e0f1

Add eval

Browse files

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -5,6 +5,19 @@ metrics:
 pipeline_tag: text-generation
 tags:
 - code
 ---
 # Model Card for instruct-codegen-16B
@@ -14,7 +27,7 @@ Instruct-codegen-16B is an instruction following codegen model based on [Salesfo
 The data was not generated using any commercial LLM api.
-The model achieves a new SoTA result of 36.1% pass@1 on the HumanEval benchmark.
 ## Generation

 pipeline_tag: text-generation
 tags:
 - code
+model-index:
+  - name: instruct-codegen-16B
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          type: openai_humaneval
+          name: HumanEval
+        metrics:
+          - name: pass@1
+            type: pass@1
+            value: 0.371
+            verified: false
 ---
 # Model Card for instruct-codegen-16B
 The data was not generated using any commercial LLM api.
+The model achieves a result of 37.1% pass@1 on the HumanEval benchmark.
 ## Generation