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 tokenizer_config.json from scpalmetto/Ouro-2.6B-Thinking-Fixed: direct link, hf CLI and curl.
- Browser
- Download file 4.21 kB
-
https://huggingface.co/scpalmetto/Ouro-2.6B-Thinking-Fixed/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://scpalmetto/Ouro-2.6B-Thinking-Fixed/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/scpalmetto/Ouro-2.6B-Thinking-Fixed/resolve/main/tokenizer_config.json
4.21 kB
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| "additional_special_tokens": [ | |
| "<|endoftext|>", | |
| "<|im_start|>", | |
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| "<think>", | |
| "</think>", | |
| "<file_sep>", | |
| "<filename>", | |
| "<gh_stars>", | |
| "<issue_start>", | |
| "<issue_comment>", | |
| "<issue_closed>", | |
| "<jupyter_start>", | |
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| "bos_token": "<|endoftext|>", | |
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| "chat_template": "{%- if messages[0]['role'] == 'system' -%}{{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}{%- else -%}{{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}{%- endif -%}{%- for message in messages -%}{%- if message.role == 'system' and loop.first -%}{# Skip #}{%- else -%}{{- '<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n' }}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\\n' }}{%- endif -%}", | |
| "eos_token": "<|endoftext|>", | |
| "extra_special_tokens": {}, | |
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| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>", | |
| "vocab_size": 49152 | |
| } | |