Text Generation
Transformers
Safetensors
GGUF
English
llama
hypertensor
ceci-graft
smollm2
experimental
conversational
text-generation-inference
Instructions to use NagusameCS/minHjerteven with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NagusameCS/minHjerteven with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NagusameCS/minHjerteven") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NagusameCS/minHjerteven") model = AutoModelForCausalLM.from_pretrained("NagusameCS/minHjerteven", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NagusameCS/minHjerteven 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 NagusameCS/minHjerteven:Q8_0 # Run inference directly in the terminal: llama cli -hf NagusameCS/minHjerteven:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NagusameCS/minHjerteven:Q8_0 # Run inference directly in the terminal: llama cli -hf NagusameCS/minHjerteven:Q8_0
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 NagusameCS/minHjerteven:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf NagusameCS/minHjerteven:Q8_0
Use Docker
docker model run hf.co/NagusameCS/minHjerteven:Q8_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use NagusameCS/minHjerteven with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NagusameCS/minHjerteven" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NagusameCS/minHjerteven", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NagusameCS/minHjerteven:Q8_0
- SGLang
How to use NagusameCS/minHjerteven 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 "NagusameCS/minHjerteven" \ --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": "NagusameCS/minHjerteven", "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 "NagusameCS/minHjerteven" \ --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": "NagusameCS/minHjerteven", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use NagusameCS/minHjerteven with Ollama:
ollama run hf.co/NagusameCS/minHjerteven:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use NagusameCS/minHjerteven with Docker Model Runner:
docker model run hf.co/NagusameCS/minHjerteven:Q8_0
- Lemonade
How to use NagusameCS/minHjerteven with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NagusameCS/minHjerteven:Q8_0
Run and chat with the model
lemonade run user.minHjerteven-Q8_0
List all available models
lemonade list
- Atomic Chat
Fix packaging (transformers 4.x tokenizer/RoPE, tokenizer truncation, license, base_model) and correct the model card with measured quality (audit 2026-10-09); add Q8_0 GGUF and a working Ollama Modelfile
e9eaee3 verified Download tokenizer.json from NagusameCS/minHjerteven: direct link, hf CLI and curl.
- Browser
- Download file 2.1 MB
-
https://huggingface.co/NagusameCS/minHjerteven/resolve/main/tokenizer.json
- Command line
-
hf download hf://NagusameCS/minHjerteven/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NagusameCS/minHjerteven/resolve/main/tokenizer.json
2.1 MB
File too large to display, you can check the raw version instead.