Instructions to use squ11z1/Hypnos-Colossus-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squ11z1/Hypnos-Colossus-1T with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="squ11z1/Hypnos-Colossus-1T", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("squ11z1/Hypnos-Colossus-1T", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use squ11z1/Hypnos-Colossus-1T with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="squ11z1/Hypnos-Colossus-1T",
	filename="Q3_K_M/Kimi-K2-Thinking-Q3_K_M-00001-of-00011.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use squ11z1/Hypnos-Colossus-1T 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 squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/Hypnos-Colossus-1T:Q3_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 squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_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 squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Use Docker

docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M

LM Studio
Jan

vLLM

How to use squ11z1/Hypnos-Colossus-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squ11z1/Hypnos-Colossus-1T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/Hypnos-Colossus-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M

SGLang

How to use squ11z1/Hypnos-Colossus-1T 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 "squ11z1/Hypnos-Colossus-1T" \
    --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": "squ11z1/Hypnos-Colossus-1T",
		"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 "squ11z1/Hypnos-Colossus-1T" \
        --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": "squ11z1/Hypnos-Colossus-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use squ11z1/Hypnos-Colossus-1T with Ollama:
```
ollama run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M
```

Unsloth Studio

How to use squ11z1/Hypnos-Colossus-1T with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

How to use squ11z1/Hypnos-Colossus-1T with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "squ11z1/Hypnos-Colossus-1T:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use squ11z1/Hypnos-Colossus-1T with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default squ11z1/Hypnos-Colossus-1T:Q3_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use squ11z1/Hypnos-Colossus-1T with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "squ11z1/Hypnos-Colossus-1T:Q3_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use squ11z1/Hypnos-Colossus-1T with Docker Model Runner:
```
docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M
```

Lemonade

How to use squ11z1/Hypnos-Colossus-1T with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/Hypnos-Colossus-1T:Q3_K_M

Run and chat with the model

lemonade run user.Hypnos-Colossus-1T-Q3_K_M

List all available models

lemonade list

Hypnos-Colossus-1T / config.json

squ11z1

Upload config.json with huggingface_hub

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	{
	"_attn_implementation_autoset": false,
	"_name_or_path": "squ11z1/Hypnos-Colossus-1T",
	"add_cross_attention": false,
	"architectures": [
	"DeepseekV3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_deepseek.DeepseekV3Config",
	"AutoModel": "modeling_deepseek.DeepseekV3Model",
	"AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
	},
	"aux_loss_alpha": 0.001,
	"bad_words_ids": null,
	"begin_suppress_tokens": null,
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	"hidden_act": "silu",
	"hidden_size": 7168,
	"id2label": {
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	"1": "LABEL_1"
	},
	"initializer_range": 0.02,
	"intermediate_size": 18432,
	"is_decoder": false,
	"is_encoder_decoder": false,
	"kv_lora_rank": 512,
	"label2id": {
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	"LABEL_1": 1
	},
	"length_penalty": 1.0,
	"max_length": 20,
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	"num_beam_groups": 1,
	"num_beams": 1,
	"num_experts_per_tok": 8,
	"num_hidden_layers": 61,
	"num_key_value_heads": 64,
	"num_nextn_predict_layers": 0,
	"num_return_sequences": 1,
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	"output_hidden_states": false,
	"output_scores": false,
	"pad_token_id": 163839,
	"prefix": null,
	"pretraining_tp": 1,
	"problem_type": null,
	"pruned_heads": {},
	"q_lora_rank": 1536,
	"qk_nope_head_dim": 128,
	"qk_rope_head_dim": 64,
	"quantization_config": {
	"config_groups": {
	"group_0": {
	"input_activations": null,
	"output_activations": null,
	"targets": [
	"Linear"
	],
	"weights": {
	"actorder": null,
	"block_structure": null,
	"dynamic": false,
	"group_size": 32,
	"num_bits": 4,
	"observer": "minmax",
	"observer_kwargs": {},
	"strategy": "group",
	"symmetric": true,
	"type": "int"
	}
	}
	},
	"format": "pack-quantized",
	"ignore": [
	"lm_head",
	"re:.self_attn.",
	"re:.shared_experts.",
	"re:.mlp\\.(gate\|up\|gate_up\|down)_proj."
	],
	"kv_cache_scheme": null,
	"quant_method": "compressed-tensors",
	"quantization_status": "compressed"
	},
	"remove_invalid_values": false,
	"repetition_penalty": 1.0,
	"return_dict": true,
	"return_dict_in_generate": false,
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"beta_fast": 1.0,
	"beta_slow": 1.0,
	"factor": 64.0,
	"mscale": 1.0,
	"mscale_all_dim": 1.0,
	"original_max_position_embeddings": 4096,
	"type": "yarn"
	},
	"rope_theta": 50000.0,
	"routed_scaling_factor": 2.827,
	"scoring_func": "sigmoid",
	"sep_token_id": null,
	"seq_aux": true,
	"suppress_tokens": null,
	"task_specific_params": null,
	"temperature": 1.0,
	"tf_legacy_loss": false,
	"tie_encoder_decoder": false,
	"tie_word_embeddings": false,
	"tokenizer_class": null,
	"top_k": 50,
	"top_p": 1.0,
	"topk_group": 1,
	"topk_method": "noaux_tc",
	"torch_dtype": "bfloat16",
	"torchscript": false,
	"transformers_version": "4.51.3",
	"typical_p": 1.0,
	"use_bfloat16": false,
	"use_cache": true,
	"v_head_dim": 128,
	"vocab_size": 163840,
	"quantum_backend": "ibm_fez",
	"quantization_method": "INT4 Native + Quantum Scale Perturbation"
	}