Pluto

Pluto is a 9B parameter coding and reasoning model developed by Merlin Research, built for precision, robustness, and seamless deployment in agentic coding environments including Claude Code, OpenAI Codex, and local large-codebase workflows.

---

Model Summary

---

Key Features

🎯 Precision-First Design

Pluto is trained to minimize errors rather than maximize fluency. Every training signal — from distillation targets to RL reward shaping — is oriented around correctness, not surface-level coherence. This makes Pluto particularly effective for tasks where a single wrong line of code has downstream consequences.

🔭 1M Token Context

Pluto supports up to 1,000,000 tokens of context, enabling operation on large codebases without chunking or retrieval hacks. Feed it an entire repository, a multi-file diff, or a long conversation history — Pluto maintains coherent reasoning across the full window.

🤖 Agentic Deployment Ready

Pluto is fine-tuned specifically for deployment in:

• Claude Code — system prompt formatting, tool call patterns, multi-turn agentic loops
• OpenAI Codex / Assistants API — compatible message structure and function calling behavior
• Local deployment — GGUF and quantized variants available for running against large local codebases without API latency

⚛️ Quantum Entropy Regularization (AER)

During RL training, Pluto used Adaptive Entropy Regularization (AER) with quantum noise sourced from the IBM Quantum Kingston processor (Heron r2, 156 qubits). Bitstring measurements from entangled quantum states were used to modulate the per-token entropy coefficient λ(t) during GRPO training, providing:

• Resistance to entropy collapse and reward hacking
• Improved robustness on out-of-distribution inputs
• More stable training dynamics across long RL runs

This makes Pluto the first production coding model trained with quantum hardware-sourced entropy regularization.

📚 Distillation from Frontier Models

Pluto was trained using knowledge distillation from multiple frontier coding models, combined with a curated private dataset of advanced reasoning traces. The distillation pipeline transfers deep reasoning chains from teacher models while keeping inference cost at the 9B scale.

---

Quickstart

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "MerlinSafety/Pluto"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "Write a Python function that parses a JWT token without external libraries and validates the expiry timestamp."
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=2048,
        temperature=0.6,
        top_p=0.95,
        do_sample=True,
        repetition_penalty=1.1,
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

With Unsloth (faster inference, 4-bit)

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="MerlinSafety/Pluto",
    max_seq_length=131072,  # adjust as needed
    dtype=None,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

messages = [
    {"role": "user", "content": "Refactor this function to be async and add proper error handling:\n\ndef fetch_data(url):\n    import requests\n    return requests.get(url).json()"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=1024,
    temperature=0.6,
    do_sample=True,
)

print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

GGUF / llama.cpp (local deployment)

# Download Q4_K_M (recommended, ~5.4GB)
huggingface-cli download MerlinSafety/Pluto \
    Pluto-Q4_K_M.gguf \
    --local-dir ./pluto

# Download Q8_0 (higher quality, ~9.4GB)
huggingface-cli download MerlinSafety/Pluto \
    Pluto-Q8_0.gguf \
    --local-dir ./pluto

# Run with llama.cpp
./llama-cli \
    -m ./pluto/Pluto-Q4_K_M.gguf \
    -p "Explain the time complexity of this algorithm and suggest optimizations:\n[your code here]" \
    -n 1024 \
    --temp 0.6 \
    --top-p 0.95 \
    -c 8192

Ollama

cat > Modelfile << 'EOF'
FROM ./Pluto-Q4_K_M.gguf
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER num_ctx 8192
EOF

ollama create pluto -f Modelfile
ollama run pluto "Write a thread-safe singleton implementation in Python"

---

Claude Code Integration

Pluto is optimized for use as a local backend in Claude Code via the --model flag when pointing to a local OpenAI-compatible server:

# Start local server (example with llama.cpp server)
./llama-server \
    -m pluto-9b-q4_k_m.gguf \
    --port 8080 \
    -c 32768 \
    --chat-template qwen

# Use with Claude Code
claude --model http://localhost:8080 "Review this PR and identify potential bugs"

---

OpenAI Codex / Assistants API Integration

Pluto's instruction format is compatible with the OpenAI Chat Completions API when served through a compatible endpoint:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",  # your local Pluto server
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="pluto",
    messages=[
        {
            "role": "user",
            "content": "Write a SQL query to find the top 5 customers by revenue in the last 30 days, handling NULL values correctly."
        }
    ],
    max_tokens=1024,
    temperature=0.6,
)

print(response.choices[0].message.content)

---

---

Training Details

Pipeline Overview

Qwen/Qwen3.5-9B-Base
    │
    ▼
SFT on curated advanced reasoning + coding dataset
(private dataset, distillation from frontier models)
    │
    ▼
GRPO Reinforcement Learning
with Adaptive Entropy Regularization (AER)
+ IBM Quantum Kingston entropy noise injection
    │
    ▼
Long-context fine-tuning (1M token extension)
    │
    ▼
Agentic deployment fine-tuning
(Claude Code + Codex format alignment)
    │
    ▼
Pluto 9B

Adaptive Entropy Regularization (AER)

During RL training, the loss function was modified as:

L_total = L_RL + λ(t) · L_entropy

where λ(t) is a dynamic coefficient modulated by quantum bitstring measurements from the IBM Quantum Kingston (Heron r2) processor. GHZ-state measurements provided true quantum randomness that guided the per-token entropy targets, preventing entropy collapse and improving robustness.

Compute

Training was conducted on Google Cloud TPU/GPU infrastructure supported by a Google TPU Research Cloud (TRC) grant awarded to Merlin Research.

---

Intended Use

• Complex code generation and refactoring
• Multi-file codebase analysis
• Agentic coding pipelines (Claude Code, Codex)
• Code review and bug detection
• Architecture planning and technical reasoning
• Local deployment with large private codebases

---

Limitations

• Pluto is optimized for coding and technical reasoning — general conversation and creative tasks are outside its primary design goal
• Like all LLMs, Pluto can produce incorrect code; always review generated output before deploying to production
• Performance on very niche frameworks or proprietary APIs may be limited by training data coverage
• Quantum entropy component provides training-time benefits; inference behavior is classical

---

Citation

@misc{pluto-2026,
  title={Pluto: Precision Coding and Reasoning Model with Quantum Entropy Regularization},
  author={Merlin Research},
  year={2026},
  publisher={Merlin Research},
  url={https://huggingface.co/MerlinSafety/Pluto}
}

---

About Merlin Research

Merlin Research is an independent AI safety laboratory based in Stockholm, Sweden, focused on open-source model development, adaptive entropy regularization, and practical AI alignment. Our models are released publicly to advance accessible, safe, and high-quality AI for the research community.

HuggingFace: huggingface.co/MerlinSafety
Contact: MerlinResearch@protonmail.com