Instructions to use FourOhFour/Deedlit_4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FourOhFour/Deedlit_4B with Transformers:

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

pipe = pipeline("text-generation", model="FourOhFour/Deedlit_4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FourOhFour/Deedlit_4B")
model = AutoModelForCausalLM.from_pretrained("FourOhFour/Deedlit_4B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FourOhFour/Deedlit_4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FourOhFour/Deedlit_4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FourOhFour/Deedlit_4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FourOhFour/Deedlit_4B

SGLang

How to use FourOhFour/Deedlit_4B 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 "FourOhFour/Deedlit_4B" \
    --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": "FourOhFour/Deedlit_4B",
		"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 "FourOhFour/Deedlit_4B" \
        --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": "FourOhFour/Deedlit_4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FourOhFour/Deedlit_4B with Docker Model Runner:
```
docker model run hf.co/FourOhFour/Deedlit_4B
```

jeiku commited on Sep 2, 2024

Commit

62f5635

verified ·

1 Parent(s): 64edde6

Update README.md

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README.md CHANGED Viewed

@@ -6,6 +6,16 @@ library_name: transformers
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/LO_cHvHzzqkq705Tf4NQc.png)
 This model was created with the help of several members of Anthracite.
 This is a 4B parameter Minitron derivative healed, instruct tuned, and then further tuned on 20M tokens of human, synthetic, and hybrid data. This model was tuned at 16k context during all steps. This model should perform well as a general assistant and RP model.

 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/LO_cHvHzzqkq705Tf4NQc.png)
+```
+|      Groups      |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
+|------------------|------:|------|------|------|---|-----:|---|-----:|
+|mmlu              |      2|none  |      |acc   |_  |0.5847|_  |0.0039|
+| - humanities     |      2|none  |      |acc   |_  |0.5345|_  |0.0068|
+| - other          |      2|none  |      |acc   |_  |0.6482|_  |0.0082|
+| - social sciences|      2|none  |      |acc   |_  |0.6822|_  |0.0082|
+| - stem           |      2|none  |      |acc   |_  |0.5021|_  |0.0086|
+```
 This model was created with the help of several members of Anthracite.
 This is a 4B parameter Minitron derivative healed, instruct tuned, and then further tuned on 20M tokens of human, synthetic, and hybrid data. This model was tuned at 16k context during all steps. This model should perform well as a general assistant and RP model.