---
license: apache-2.0
base_model:
  - amd/Instella-MoE-16B-A3B-SFT
datasets:
  - Jackrong/Competitive-Programming-python-blend
  - Jackrong/qwen3-coder-480b-distill-mini
  - Jackrong/DeepSeek-V4-Distill-8000x
  - angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
tags:
  - instella
  - moe
  - coding
  - python
  - distillation
  - code-generation
  - transformers
  - pytorch
  - safetensors
  - stamsam-labs
pipeline_tag: text-generation
library_name: transformers
model-index:
  - name: Instella-Prometheus
    results:
      - task:
          type: text-generation
          name: Code Generation (Bare Prompt)
        dataset:
          type: custom
          name: 12-task Python Code Suite
        metrics:
          - type: functional_passes
            value: 10
            name: Functional Passes (12 tasks)
          - type: code_blocks
            value: 12
            name: Code Blocks Generated
          - type: syntax_valid
            value: 12
            name: Syntax Valid
          - type: think_leak
            value: 0
            name: Think Tag Leak
          - type: natural_eos
            value: 12
            name: Natural EOS Completion
---

# Instella-Prometheus 🔥

![Instella-Prometheus](prometheus.png)

**Stole code-fire from the gods. Gave it to the people.**

No system prompt. No thinking tags. No decoder bans. No adapter dependencies. Just a bare user prompt and clean Python.

**`stamsam/Instella-Prometheus`** — 16B total / 2.8B active parameters — fine-tuned on powerhouse Python code traces from four teacher models and fused permanently into the base weights. It loads with a single `from_pretrained()` call and zero extra dependencies.

## Four Ways to Run It

This repository contains four downloadable weight variants. Choose the one that fits your hardware and runtime:

| Variant | File | Approx. size | Best for |
|---|---|---:|---|
| **Full Transformers weights** | 7 `model-0000X-of-00007.safetensors` shards | ~30 GB | `transformers`, training, maximum flexibility |
| **Q8_0 GGUF** | `Instella-Prometheus-Q8_0.gguf` | ~16.9 GB | Near-full-quality llama.cpp inference |
| **Q4_K_M GGUF** | `Instella-Prometheus-Q4_K_M.gguf` | ~9.4 GB | Best quality/size balance |
| **Q3_K_M GGUF** | `Instella-Prometheus-Q3_K_M.gguf` | ~8.2 GB | Lower-memory llama.cpp inference |

The three `.gguf` files are in the repository root so Hugging Face can identify them as quantized variants. The GGUF files are standalone: they include the model metadata needed by llama.cpp and do not require the Transformers weights.

## The Theft

The gods kept their fire locked away — DeepSeek V4, Qwen 3 Coder 480B, Claude Opus, and the competitive programming pantheon. Each held a piece of what it means to write clean, functional Python. Prometheus stole from all of them, distilled their fire into 26,028 records of AST-valid, think-free, code-first answers, and baked it into a single 30GB artifact that answers to no system prompt and bows to no decoder ban.

## The Proof

| Metric | Base Instella | **Instella-Prometheus** | Bar |
|---|---|---|---|
| Code blocks generated | 2 / 12 | **12 / 12** | — |
| Syntax valid | 2 / 12 | **12 / 12** | — |
| Functional passes | 2 / 12 | **10 / 12** | ≥9 ✅ |
| Think tag leak | 12 / 12 | **0 / 12** | ≤3 ✅ |
| Natural EOS | 2 / 12 | **12 / 12** | — |

The base Instella-MoE-SFT produces almost no code under a bare prompt — it defaults to thinking tags and narrative text. Prometheus writes clean, functional Python directly, with zero thinking leakage and perfect EOS completion. That's a **5× improvement in functional passes** and a **complete elimination of think-tag leakage** — no system prompt, no decoder bans, no adapter dependencies.

## The Training

Three phases, each building on the last:

1. **v1** — 4,000 records from agentic coding traces (kimi-k3, fable-5, hermes-agent). Proved the concept: 4/12 functional, 11/12 think leak.
2. **v2** — 300 steps continuation from v1 checkpoint-500. Same data, bare prompt. Hit 11/12 functional, 0/12 think — but the dataset was too small to bake it in permanently.
3. **v3 (powerhouse)** — 300 steps from v2 checkpoint-100 on a 26,028-record powerhouse pool. The LoRA was merged permanently into the base weights. The result: 10/12 functional, 0/12 think, 12/12 EOS — standalone, no crutches.

| Source | Teacher | Records | License |
|---|---|---|---|
| Competitive-Programming-python-blend | Contest problems (multi) | 15,014 | Permissive mix |
| qwen3-coder-480b-distill-mini | Qwen 3 Coder 480B | 8,303 | Apache-2.0 |
| DeepSeek-V4-Distill-8000x | DeepSeek V4 | 2,148 | MIT |
| Claude-Opus 4.6/4.7 (code split) | Claude Opus | 454 | Apache-2.0 |
| v1 unique carryover | (kimi-k3, fable-5, hermes) | 109 | CC-BY-4.0 |

Every record: AST-valid Python, no thinking tags, code-first, provenance preserved.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "stamsam/Instella-Prometheus",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "stamsam/Instella-Prometheus",
    trust_remote_code=True,
)

# No system prompt. No thinking preamble. Just the task.
prompt = "Write a function to merge overlapping intervals."
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(
    inputs,
    max_new_tokens=1024,
    do_sample=False,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
```

## GGUF Quantizations

The three GGUF quantizations are in the repository root and are suitable for llama.cpp and its bindings.

| File | Size | BPW |
|---|---:|---:|
| `Instella-Prometheus-Q8_0.gguf` | 16.9 GB | 8.94 |
| `Instella-Prometheus-Q4_K_M.gguf` | 9.4 GB | 5.28 |
| `Instella-Prometheus-Q3_K_M.gguf` | 8.2 GB | 4.14 |

For the original Transformers format, use the seven `model-0000X-of-00007.safetensors` shards plus `model.safetensors.index.json`.

**Release note:** This repository is the canonical full-weights release. The standalone GGUF companion is available at [`stamsam/Instella-Prometheus-GGUF`](https://huggingface.co/stamsam/Instella-Prometheus-GGUF).

## License

Apache-2.0. The base model (Instella-MoE-16B-A3B-SFT) is released by AMD under a research-permissive license. Training data sources carry their own licenses (CC-BY-4.0, Apache-2.0, MIT, BSD) — all permissive for redistribution.

## Acknowledgements

Built on the shoulders of giants: AMD (Instella-MoE), greghavens (coding traces), Jackrong (distillation datasets), angrygiraffe (Opus reasoning traces), and the open-source community. The fire belongs to them. The torch is yours.