Instructions to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity with Transformers:

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

pipe = pipeline("text-generation", model="CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity")
model = AutoModelForCausalLM.from_pretrained("CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity

SGLang

How to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity 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 "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity 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 CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity 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 CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity",
    max_seq_length=2048,
)

Docker Model Runner
How to use CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity with Docker Model Runner:
```
docker model run hf.co/CompassioninMachineLearning/pretrainingBaseLlama3kv3UrbanDensity
```

pretrainingBaseLlama3kv3UrbanDensity

File size: 900 Bytes

76bb4af

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "torch_dtype": "bfloat16",
  "eos_token_id": 128001,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 131072,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pad_token_id": 128004,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": {
    "factor": 8.0,
    "high_freq_factor": 4.0,
    "low_freq_factor": 1.0,
    "original_max_position_embeddings": 8192,
    "rope_type": "llama3"
  },
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "transformers_version": "4.57.2",
  "unsloth_version": "2025.6.12",
  "use_cache": true,
  "vocab_size": 128256
}