Text Generation
Transformers
Safetensors
GGUF
English
qwen3.5
gated-deltanet
hybrid-attention
dense
tinystories
tiny-model
conversational
Instructions to use shibatch/tinyqwen35dense2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibatch/tinyqwen35dense2m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shibatch/tinyqwen35dense2m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/tinyqwen35dense2m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use shibatch/tinyqwen35dense2m with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf shibatch/tinyqwen35dense2m:Q4_K_M # Run inference directly in the terminal: llama cli -hf shibatch/tinyqwen35dense2m:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf shibatch/tinyqwen35dense2m:Q4_K_M # Run inference directly in the terminal: llama cli -hf shibatch/tinyqwen35dense2m:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf shibatch/tinyqwen35dense2m:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf shibatch/tinyqwen35dense2m:Q4_K_M
Use Docker
docker model run hf.co/shibatch/tinyqwen35dense2m:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use shibatch/tinyqwen35dense2m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shibatch/tinyqwen35dense2m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shibatch/tinyqwen35dense2m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shibatch/tinyqwen35dense2m:Q4_K_M
- SGLang
How to use shibatch/tinyqwen35dense2m 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 "shibatch/tinyqwen35dense2m" \ --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": "shibatch/tinyqwen35dense2m", "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 "shibatch/tinyqwen35dense2m" \ --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": "shibatch/tinyqwen35dense2m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use shibatch/tinyqwen35dense2m with Ollama:
ollama run hf.co/shibatch/tinyqwen35dense2m:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use shibatch/tinyqwen35dense2m with Docker Model Runner:
docker model run hf.co/shibatch/tinyqwen35dense2m:Q4_K_M
- Lemonade
How to use shibatch/tinyqwen35dense2m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shibatch/tinyqwen35dense2m:Q4_K_M
Run and chat with the model
lemonade run user.tinyqwen35dense2m-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,214 Bytes
0bd3079 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | """Generate a short TinyStories-style continuation from the local HF checkpoint."""
from pathlib import Path
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def main() -> None:
model_dir = Path(__file__).resolve().parent / "hf"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir, dtype=torch.float32).to(device)
model.eval()
prompt = "Once upon"
input_ids = torch.tensor(
[[tokenizer.bos_token_id] + tokenizer.encode(prompt, add_special_tokens=False)],
dtype=torch.long,
device=device,
)
torch.manual_seed(1234)
with torch.no_grad():
output = model.generate(
input_ids=input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.8,
top_p=0.95,
top_k=40,
repetition_penalty=1.1,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
if __name__ == "__main__":
main()
|