Instructions to use budecosystem/code-millenials-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/code-millenials-8b with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/code-millenials-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-8b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-8b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use budecosystem/code-millenials-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/code-millenials-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/budecosystem/code-millenials-8b

SGLang

How to use budecosystem/code-millenials-8b 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-8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use budecosystem/code-millenials-8b with Docker Model Runner:
```
docker model run hf.co/budecosystem/code-millenials-8b
```

dittops commited on Apr 21, 2024

Commit

3be3888

verified ·

1 Parent(s): 85d673e

Update README.md

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@@ -85,20 +85,20 @@ print(tokenizer.decode(sample[0]))
 ## Training details
-The model is trained of 16 A100 80GB for approximately 50hrs.
 | Hyperparameters              | Value  |
 | :----------------------------| :-----: |
-| per_device_train_batch_size  | 16      |
 | gradient_accumulation_steps  | 1      |
 | epoch | 3 |
-| steps | 2157 |
 | learning_rate                | 2e-5   |
 | lr schedular type | cosine |
 | warmup ratio | 0.1 |
 | optimizer                    | adamw  |
 | fp16                         | True   |
-| GPU                          | 16 A100 80GB |
 ### Important Note

 ## Training details
+The model is trained of 8 A100 80GB for approximately 50hrs.
 | Hyperparameters              | Value  |
 | :----------------------------| :-----: |
+| per_device_train_batch_size  | 8      |
 | gradient_accumulation_steps  | 1      |
 | epoch | 3 |
+| steps | 8628 |
 | learning_rate                | 2e-5   |
 | lr schedular type | cosine |
 | warmup ratio | 0.1 |
 | optimizer                    | adamw  |
 | fp16                         | True   |
+| GPU                          | 8 A100 80GB |
 ### Important Note