Instructions to use stamsam/Instella-Prometheus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stamsam/Instella-Prometheus with Transformers:

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

pipe = pipeline("text-generation", model="stamsam/Instella-Prometheus", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stamsam/Instella-Prometheus", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stamsam/Instella-Prometheus", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use stamsam/Instella-Prometheus 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 stamsam/Instella-Prometheus:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf stamsam/Instella-Prometheus:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf stamsam/Instella-Prometheus:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf stamsam/Instella-Prometheus: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 stamsam/Instella-Prometheus:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf stamsam/Instella-Prometheus: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 stamsam/Instella-Prometheus:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf stamsam/Instella-Prometheus:Q4_K_M

Use Docker

docker model run hf.co/stamsam/Instella-Prometheus:Q4_K_M

LM Studio
Jan

vLLM

How to use stamsam/Instella-Prometheus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stamsam/Instella-Prometheus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stamsam/Instella-Prometheus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stamsam/Instella-Prometheus:Q4_K_M

SGLang

How to use stamsam/Instella-Prometheus 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 "stamsam/Instella-Prometheus" \
    --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": "stamsam/Instella-Prometheus",
		"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 "stamsam/Instella-Prometheus" \
        --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": "stamsam/Instella-Prometheus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use stamsam/Instella-Prometheus with Ollama:
```
ollama run hf.co/stamsam/Instella-Prometheus:Q4_K_M
```

Unsloth Studio

How to use stamsam/Instella-Prometheus 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 stamsam/Instella-Prometheus 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 stamsam/Instella-Prometheus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for stamsam/Instella-Prometheus to start chatting

Atomic Chat new
Docker Model Runner
How to use stamsam/Instella-Prometheus with Docker Model Runner:
```
docker model run hf.co/stamsam/Instella-Prometheus:Q4_K_M
```

Lemonade

How to use stamsam/Instella-Prometheus with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull stamsam/Instella-Prometheus:Q4_K_M

Run and chat with the model

lemonade run user.Instella-Prometheus-Q4_K_M

List all available models

lemonade list

Instella-Prometheus

File size: 1,889 Bytes

1cb3a94

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  "apply_all_reduce": false,
  "architectures": [
    "InstellaMoEForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_only_farskip": false,
  "auto_map": {
    "AutoConfig": "configuration_instella_moe.InstellaMoEConfig",
    "AutoModel": "modeling_instella_moe.InstellaMoEModel",
    "AutoModelForCausalLM": "modeling_instella_moe.InstellaMoEForCausalLM"
  },
  "aux_loss_alpha": 0.001,
  "bos_token_id": 1,
  "dtype": "bfloat16",
  "eos_token_id": 2,
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  "farskip_start_idx": 0,
  "first_k_dense_replace": 1,
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  "hidden_act": "silu",
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  "initializer_range": 0.02,
  "intermediate_size": 10944,
  "kv_lora_rank": 512,
  "max_position_embeddings": 32768,
  "mlp_only_farskip": false,
  "model_type": "deepseek_v3",
  "moe_intermediate_size": 1408,
  "moe_layer_freq": 1,
  "n_group": 1,
  "n_routed_experts": 64,
  "n_shared_experts": 2,
  "norm_topk_prob": true,
  "num_attention_heads": 16,
  "num_experts_per_tok": 6,
  "num_hidden_layers": 27,
  "num_key_value_heads": 16,
  "num_nextn_predict_layers": 0,
  "pretraining_tp": 1,
  "q_lora_rank": null,
  "qk_head_dim": 128,
  "qk_layernorm": true,
  "qk_nope_head_dim": 96,
  "qk_rope_head_dim": 32,
  "rms_norm_eps": 1e-06,
  "rope_interleave": true,
  "rope_scaling": {
    "beta_fast": 32,
    "beta_slow": 1,
    "factor": 40,
    "mscale": 1.0,
    "mscale_all_dim": 1.0,
    "original_max_position_embeddings": 4096,
    "type": "yarn"
  },
  "rope_theta": 8000000,
  "routed_scaling_factor": 2.5,
  "scoring_func": "sigmoid",
  "seq_aux": true,
  "tie_word_embeddings": false,
  "topk_group": 1,
  "topk_method": "noaux_tc",
  "tp_split": null,
  "transformers_version": "4.57.6",
  "use_cache": true,
  "v_head_dim": 128,
  "verbose": false,
  "vocab_size": 128896
}