Instructions to use scpalmetto/Ouro-2.6B-Thinking-Fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

scpalmetto commited on Feb 21

Commit

3546ce8

verified ·

1 Parent(s): 316966c

Upload config.json.backup with huggingface_hub

Browse files

Files changed (1) hide show

config.json.backup +88 -0

config.json.backup ADDED Viewed

	@@ -0,0 +1,88 @@

+{
+  "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": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "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
+}