Instructions to use starble-dev/Starlight-V3-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use starble-dev/Starlight-V3-12B with Transformers:

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

pipe = pipeline("text-generation", model="starble-dev/Starlight-V3-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("starble-dev/Starlight-V3-12B")
model = AutoModelForCausalLM.from_pretrained("starble-dev/Starlight-V3-12B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use starble-dev/Starlight-V3-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "starble-dev/Starlight-V3-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "starble-dev/Starlight-V3-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/starble-dev/Starlight-V3-12B

SGLang

How to use starble-dev/Starlight-V3-12B 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 "starble-dev/Starlight-V3-12B" \
    --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": "starble-dev/Starlight-V3-12B",
		"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 "starble-dev/Starlight-V3-12B" \
        --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": "starble-dev/Starlight-V3-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use starble-dev/Starlight-V3-12B with Docker Model Runner:
```
docker model run hf.co/starble-dev/Starlight-V3-12B
```

starble-dev commited on Aug 10, 2024

Commit

8dd53a2

verified ·

1 Parent(s): eeaa3d5

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@@ -1,3 +1,45 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+tags:
+- mistral
+- conversational
+- text-generation-inference
+base_model:
+- UsernameJustAnother/Nemo-12B-Marlin-v5
+- anthracite-org/magnum-12b-v2
+library_name: transformers
+---
+> [!WARNING]
+> **General Use Sampling:**<br>
+> Mistral-Nemo-12B is very sensitive to the temperature sampler, try values near **0.3** at first or else you will get some weird results. This is mentioned by MistralAI at their [Transformers](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407#transformers) section.
+> [!NOTE]
+> **Best Samplers:**<br>
+> I found best success using the following for Starlight-V3-12B:<br>
+> Temperature: `0.7`-`1.2` (Additional stopping strings will be necessary as you increase the temperature)<br>
+> Top K: `-1`<br>
+> Min P: `0.05`<br>
+> Rep Penalty: `1.03-1.1`
+# Why Version 3?
+Currently the other versions resulted in really bad results that I didn't upload them, the version number is just the internal version.
+# Goal
+The idea is to keep the strengths of [anthracite-org/magnum-12b-v2](https://huggingface.co/anthracite-org/magnum-12b-v2) while adding some more creativity
+that seems to be lacking in the model. Mistral-Nemo by itself seems to behave less sporadic due to the low temperature needed but this gets a bit repetitive,
+although it's still the best model I've used so far.
+# Results
+I am not entirely pleased with the result of the merge but it seems okay, though base [anthracite-org/magnum-12b-v2](https://huggingface.co/anthracite-org/magnum-12b-v2)
+might just be better by itself. However, I'll still experiement on different merge methods.
+Leaking of the training data used on both models seems a bit more apparent when using higher temperature values,
+especially the use of author notes on the system prompt. Generally I'd advise to create a stopping string for "```" to avoid the generation of the training data.
+**Original Models:**
+- [UsernameJustAnother/Nemo-12B-Marlin-v5](https://huggingface.co/UsernameJustAnother/Nemo-12B-Marlin-v5) (Thank you so much for your work ♥)
+- [anthracite-org/magnum-12b-v2](https://huggingface.co/anthracite-org/magnum-12b-v2) (Thank you so much for your work ♥)
+**Official Quants:** [starble-dev/Starlight-V3-12B-GGUF](https://huggingface.co/starble-dev/Starlight-V3-12B-GGUF) (Currently uploading)
+**Original Model Licenses & This Model License:** Apache 2.0