Instructions to use CrowdMind/PrimeMind-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CrowdMind/PrimeMind-0.8B with Transformers:

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

pipe = pipeline("text-generation", model="CrowdMind/PrimeMind-0.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CrowdMind/PrimeMind-0.8B")
model = AutoModelForCausalLM.from_pretrained("CrowdMind/PrimeMind-0.8B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CrowdMind/PrimeMind-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CrowdMind/PrimeMind-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CrowdMind/PrimeMind-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CrowdMind/PrimeMind-0.8B

SGLang

How to use CrowdMind/PrimeMind-0.8B 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 "CrowdMind/PrimeMind-0.8B" \
    --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": "CrowdMind/PrimeMind-0.8B",
		"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 "CrowdMind/PrimeMind-0.8B" \
        --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": "CrowdMind/PrimeMind-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CrowdMind/PrimeMind-0.8B with Docker Model Runner:
```
docker model run hf.co/CrowdMind/PrimeMind-0.8B
```

PrimeMind-0.8B

Compressed reasoning model based on Qwen/Qwen3.5-0.8B, fine-tuned on caveman-style thinking datasets.

What it does

Teaches the model to think in short, concise, information-packed style using <think> tags, then output clean answers.

Training

Base model: Qwen/Qwen3.5-0.8B
Method: LoRA SFT (4-bit NF4 quantization)
Datasets:
- catsaresupercool/synthetic-caveman-thinking (600 samples)
- nibauman/objectnav-sft-claude-caveman (600 samples, multimodal)
Total samples: 1,200
Epochs: 1
Loss: 4.81 → 1.56
Training time: ~38 minutes on RTX 4060 Ti (16GB)

LoRA Config

Rank: 64
Alpha: 128
Dropout: 0.05
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Usage

from transformers import AutoProcessor, AutoModelForMultimodalLM

model = AutoModelForMultimodalLM.from_pretrained(
    "CrowdMind/PrimeMind-0.8B",
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained("CrowdMind/PrimeMind-0.8B", trust_remote_code=True)

messages = [{"role": "user", "content": "Explain quantum computing in one sentence."}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=256)
response = processor.tokenizer.decode(output[0][inputs["input_ids"].shape[1]:])
print(response)

Downloads last month: -

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for CrowdMind/PrimeMind-0.8B

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(245)

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