Text Generation
Transformers
Safetensors
GGUF
PyTorch
deepseek_v3
instella
Mixture of Experts
coding
python
distillation
code-generation
stamsam-labs
conversational
custom_code
Eval Results (legacy)
text-generation-inference
Instructions to use stamsam/Instella-Prometheus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "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, | |
| "ep_size": 1, | |
| "farskip": true, | |
| "farskip_end_idx": 10000.0, | |
| "farskip_start_idx": 0, | |
| "first_k_dense_replace": 1, | |
| "gated_attention": true, | |
| "head_dim": 32, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "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 | |
| } | |