Instructions to use rahul05ranjan/hydra-nano-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rahul05ranjan/hydra-nano-test with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("rahul05ranjan/hydra-nano-test")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use rahul05ranjan/hydra-nano-test with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rahul05ranjan/hydra-nano-test"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rahul05ranjan/hydra-nano-test"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use rahul05ranjan/hydra-nano-test with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "rahul05ranjan/hydra-nano-test"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rahul05ranjan/hydra-nano-test"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "rahul05ranjan/hydra-nano-test",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use rahul05ranjan/hydra-nano-test with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rahul05ranjan/hydra-nano-test"

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 rahul05ranjan/hydra-nano-test

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rahul05ranjan/hydra-nano-test with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rahul05ranjan/hydra-nano-test"

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 "rahul05ranjan/hydra-nano-test" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Hydra Nano Test (`hydra-nano-test`)

Hydra Nano Test is an enterprise-grade build & test log digest engine. It ingests thousands of lines of verbose compiler and test runner output (Vitest, Jest, Pytest, Cargo, Go, TSC, Mocha) and extracts verbatim failure lines, assertion diffs, and source locations while eliminating noisy internal framework traces.

🏛️ Executive Summary & Value Proposition

Verbose test failures and build outputs are among the largest context polluters for AI agents, often consuming tens of thousands of tokens per run.

Hydra Nano Test delivers high-density diagnostic summaries:

100% Assertion & Location Recall: Retains exact user file paths, line numbers, and failure diffs.
Noise Cleanliness: Strips runner internals, node_modules stack frames, and duplicate traces.
Massive Context Savings: Compresses verbose test logs by up to 96.7%.

📊 Proven Benchmark & Evaluation

Evaluated across the 20-task cross-language benchmark (evals/test_logs/):

Metric	Score	Note
Task Solved Rate	100% (20/20)	All failing assertions and files extracted
Test Name Recall	100%	Exact failing test cases identified
File:Line Location Recall	100%	Source file and line numbers pinpointed
Assertion Diff Recall	100%	Key error diff/assertion values preserved verbatim
Stack Cleanliness	100%	Zero internal framework frames in agent context
Token Compression	Up to 96.7%	Compresses 1,000+ token test logs down to ~30 tokens

🚀 Quickstart & Usage

Via Ollama

ollama run rahul05ranjan/hydra-nano-test

Via Hydra CLI

hydra run -- npm test
hydra run -- cargo test
hydra run -- pytest

📜 Specifications

Base Architecture: Qwen3.5-0.8B
Parameters: 752.39M
Precision: BF16 / INT8
License: Apache 2.0

Downloads last month: 13

Safetensors

Model size

0.9B params

Tensor type

F32

BF16

MLX

Hardware compatibility

Quantized

Model tree for rahul05ranjan/hydra-nano-test

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(498)

this model

Hydra Nano Test (hydra-nano-test)

🏛️ Executive Summary & Value Proposition

📊 Proven Benchmark & Evaluation

🚀 Quickstart & Usage

Via Ollama

Via Hydra CLI

📜 Specifications

Model tree for rahul05ranjan/hydra-nano-test

Hydra Nano Test (`hydra-nano-test`)