Text Generation
Transformers
Safetensors
GGUF
llama
looped-language-model
reasoning
recurrent-depth
thinking
chain-of-thought
conversational
custom_code
text-generation-inference
Instructions to use scpalmetto/Ouro-2.6B-Thinking-Fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="scpalmetto/Ouro-2.6B-Thinking-Fixed", trust_remote_code=True) 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("scpalmetto/Ouro-2.6B-Thinking-Fixed", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("scpalmetto/Ouro-2.6B-Thinking-Fixed", trust_remote_code=True, 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 scpalmetto/Ouro-2.6B-Thinking-Fixed 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 scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M # Run inference directly in the terminal: llama cli -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M # Run inference directly in the terminal: llama cli -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
Use Docker
docker model run hf.co/scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "scpalmetto/Ouro-2.6B-Thinking-Fixed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "scpalmetto/Ouro-2.6B-Thinking-Fixed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
- SGLang
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed 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 "scpalmetto/Ouro-2.6B-Thinking-Fixed" \ --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": "scpalmetto/Ouro-2.6B-Thinking-Fixed", "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 "scpalmetto/Ouro-2.6B-Thinking-Fixed" \ --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": "scpalmetto/Ouro-2.6B-Thinking-Fixed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed with Ollama:
ollama run hf.co/scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed with Docker Model Runner:
docker model run hf.co/scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
- Lemonade
How to use scpalmetto/Ouro-2.6B-Thinking-Fixed with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull scpalmetto/Ouro-2.6B-Thinking-Fixed:Q4_K_M
Run and chat with the model
lemonade run user.Ouro-2.6B-Thinking-Fixed-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download config.json.backup from scpalmetto/Ouro-2.6B-Thinking-Fixed: direct link, hf CLI and curl.
- Browser
- Download file 2.07 kB
-
https://huggingface.co/scpalmetto/Ouro-2.6B-Thinking-Fixed/resolve/main/config.json.backup
- Command line
-
hf download hf://scpalmetto/Ouro-2.6B-Thinking-Fixed/config.json.backup
-
curl -L -o config.json.backup https://huggingface.co/scpalmetto/Ouro-2.6B-Thinking-Fixed/resolve/main/config.json.backup
2.07 kB
| { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_ouro.OuroConfig", | |
| "AutoModel": "modeling_ouro.OuroModel", | |
| "AutoModelForCausalLM": "modeling_ouro.OuroForCausalLM" | |
| }, | |
| "bos_token_id": 0, | |
| "eos_token_id": 0, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5632, | |
| "layer_types": [ | |
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| ], | |
| "max_position_embeddings": 65536, | |
| "max_window_layers": 48, | |
| "model_type": "llama", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 48, | |
| "num_key_value_heads": 16, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "total_ut_steps": 4, | |
| "early_exit_threshold": 1.0, | |
| "transformers_version": "4.55.0", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 49152, | |
| "_original_architecture": "OuroForCausalLM", | |
| "_loop_steps": 4 | |
| } |