Instructions to use Tijmen2/cosmosage_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tijmen2/cosmosage_v2 with Transformers:

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

pipe = pipeline("text-generation", model="Tijmen2/cosmosage_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tijmen2/cosmosage_v2")
model = AutoModelForCausalLM.from_pretrained("Tijmen2/cosmosage_v2")
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 Tijmen2/cosmosage_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tijmen2/cosmosage_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tijmen2/cosmosage_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tijmen2/cosmosage_v2

SGLang

How to use Tijmen2/cosmosage_v2 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 "Tijmen2/cosmosage_v2" \
    --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": "Tijmen2/cosmosage_v2",
		"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 "Tijmen2/cosmosage_v2" \
        --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": "Tijmen2/cosmosage_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tijmen2/cosmosage_v2 with Docker Model Runner:
```
docker model run hf.co/Tijmen2/cosmosage_v2
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+21

-7

Files changed (1) hide show

README.md +21 -7

README.md CHANGED Viewed

@@ -1,17 +1,17 @@
 ---
 tags:
 - physics
 - cosmology
 model-index:
 - name: cosmosage_qa
   results: []
-license: mit
-language:
-- en
-pipeline_tag: text-generation
-base_model: mistralai/Mistral-7B-v0.1
-datasets:
-- teknium/OpenHermes-2.5
 ---
 # cosmosage
@@ -182,3 +182,17 @@ unusual instruction format:
 > If matter-radiation equality occurs later in the history of the universe, then the photons spend more time in the diffusive state, which results in more damping of the CMB power spectrum. This is because the longer the photons spend in the diffusive state, the more time they have to lose energy through scattering with free electrons. Conversely, if matter-radiation equality occurs earlier in the history of the universe, then the photons spend less time in the diffusive state, which results in less damping of the CMB power spectrum.
 >
 > In summary, the time of matter-radiation equality affects the damping tail of the CMB power spectrum by influencing the amount of time that photons spend in the diffusive state before they are able to decouple from the matter and travel freely through the universe. The longer the photons spend in the diffusive state, the more damping occurs, and the earlier matter-radiation equality occurs, the less damping occurs.>

 ---
+language:
+- en
+license: mit
 tags:
 - physics
 - cosmology
+datasets:
+- teknium/OpenHermes-2.5
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-v0.1
 model-index:
 - name: cosmosage_qa
   results: []
 ---
 # cosmosage
 > If matter-radiation equality occurs later in the history of the universe, then the photons spend more time in the diffusive state, which results in more damping of the CMB power spectrum. This is because the longer the photons spend in the diffusive state, the more time they have to lose energy through scattering with free electrons. Conversely, if matter-radiation equality occurs earlier in the history of the universe, then the photons spend less time in the diffusive state, which results in less damping of the CMB power spectrum.
 >
 > In summary, the time of matter-radiation equality affects the damping tail of the CMB power spectrum by influencing the amount of time that photons spend in the diffusive state before they are able to decouple from the matter and travel freely through the universe. The longer the photons spend in the diffusive state, the more damping occurs, and the earlier matter-radiation equality occurs, the less damping occurs.>
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Tijmen2__cosmosage_v2)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |60.66|
+|AI2 Reasoning Challenge (25-Shot)|59.73|
+|HellaSwag (10-Shot)              |80.90|
+|MMLU (5-Shot)                    |59.57|
+|TruthfulQA (0-shot)              |50.98|
+|Winogrande (5-shot)              |75.93|
+|GSM8k (5-shot)                   |36.85|