Instructions to use budecosystem/code-millenials-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/code-millenials-13b with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/code-millenials-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-13b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use budecosystem/code-millenials-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/code-millenials-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/budecosystem/code-millenials-13b

SGLang

How to use budecosystem/code-millenials-13b 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 "budecosystem/code-millenials-13b" \
    --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": "budecosystem/code-millenials-13b",
		"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 "budecosystem/code-millenials-13b" \
        --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": "budecosystem/code-millenials-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use budecosystem/code-millenials-13b with Docker Model Runner:
```
docker model run hf.co/budecosystem/code-millenials-13b
```

dittops commited on Jan 5, 2024

Commit

71bcad7

1 Parent(s): 760e69d

Update README.md

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@@ -1,5 +1,21 @@
 ---
 license: llama2
 ---
@@ -25,10 +41,10 @@ Note: The humaneval values of other models are taken from the official repos of
 ### Models
-|   Model | Checkpoint  | HumanEval |
-|---------|-------------|-----------|
-|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 |
-|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 |

 ---
 license: llama2
+library_name: transformers
+tags:
+- code
+model-index:
+- name: Code Millenials
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.7621
+      verified: false
 ---
 ### Models
+|   Model | Checkpoint  | HumanEval (+) | MBPP (+) |
+|---------|-------------|---------------|----------|
+|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 (75) | 74.68 (62.9) |
+|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 (69.5) | 70.17 (57.6) |