Instructions to use rmdhirr/Pulsar_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rmdhirr/Pulsar_7B with Transformers:

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

pipe = pipeline("text-generation", model="rmdhirr/Pulsar_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rmdhirr/Pulsar_7B")
model = AutoModelForMultimodalLM.from_pretrained("rmdhirr/Pulsar_7B")
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 rmdhirr/Pulsar_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rmdhirr/Pulsar_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Pulsar_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rmdhirr/Pulsar_7B

SGLang

How to use rmdhirr/Pulsar_7B 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 "rmdhirr/Pulsar_7B" \
    --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": "rmdhirr/Pulsar_7B",
		"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 "rmdhirr/Pulsar_7B" \
        --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": "rmdhirr/Pulsar_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use rmdhirr/Pulsar_7B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rmdhirr/Pulsar_7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rmdhirr/Pulsar_7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rmdhirr/Pulsar_7B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rmdhirr/Pulsar_7B",
    max_seq_length=2048,
)

Docker Model Runner
How to use rmdhirr/Pulsar_7B with Docker Model Runner:
```
docker model run hf.co/rmdhirr/Pulsar_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

💫 Pulsar_7B

⚠️ This is an experimental model!

A more compliant, RP-oriented version of MTSAIR/multi_verse_model, fine-tuned on carefully selected datasets. It's smart, adept at following the desired markdown format and adhering to the provided character card. The first message of the character card significantly influences its writing style. Pulsar_7B pairs well with guidance from CFG Scale and works effectively with PLists + Ali:Chat character cards. Pulsar_7B was fine-tuned on the following datasets:

grimulkan/theory-of-mind
grimulkan/physical-reasoning
ResplendentAI/Luna_Alpaca
unalignment/toxic-dpo-v0.2
kira/math-dpo
athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED

Quantizations

Thanks to mradermacher, static GGUF quants are available here.

Formatting/Preset

Pulsar_7B works well with Alpaca, it's not a picky model when it comes to formatting/preset. Mistral should be compatible too. The custom chat template from MTSAIR/multi_verse_model also performs well:

{% for message in messages %}{% if message['role'] == 'user' %}{{ '### Instruction:\n' + message['content'] + '\n### Response:\n' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% elif message['role'] == 'system' %}{{ '### System:\n' + message['content'] + '\n' }}{% endif %}{% endfor %}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.23
AI2 Reasoning Challenge (25-Shot)	69.71
HellaSwag (10-Shot)	86.99
MMLU (5-Shot)	63.72
TruthfulQA (0-shot)	69.28
Winogrande (5-shot)	84.06
GSM8k (5-shot)	71.65