Support our open-source dataset and model releases!
Esper 4: gemma-4-12B, Qwen3.6-27B, Muse-Glimmer-30B
Esper 4 is an agentic coding, architecture, DevOps, and MLOps specialist built on Muse Glimmer 30B!
- Your dedicated DevOps expert: Esper 4 maximizes DevOps and architecture helpfulness, powered by high-difficulty DevOps and architecture data generated with DeepSeek-V4-Pro!
- Improved coding performance: challenging agentic coding queries allow Esper 4 to tackle harder coding tasks!
- AI to build AI: our high-difficulty AI coding and expertise data boosts Esper 4 for AI development, research, deployment, interpretability, operation and experimentation!
- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!
Prompting Guide
Esper 4 uses the Muse Glimmer prompt format.
Use Esper 4 with your agentic framework of choice or as a stand-alone chat and code assistant.
Example inference script to get started:
from transformers import AutoProcessor, AutoModelForMultimodalLM
MODEL_ID = "ValiantLabs/Muse-Glimmer-30B-Esper4"
# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype="auto", device_map="auto")
# Prompt
prompt = "Implement CQRS for network appliance config management.\n\nRequirements:\n- Write side: 200 commands/sec, 4 command handlers, SQLite with custom journaling\n- Read side: 1000 queries/sec, 3 read projections in shared memory segments\n- Eventual consistency window: 100ms max\n- Handle atomic swap of projection memory for rebuilds\n- Binary configuration format versioning for schema evolution\n- Framework: libevent with custom protocol parser\n\nConstraints:\n- Manual memory management only, no garbage collection\n- Lock-free data structures where possible\n- Shared memory projections must survive process restarts\n- Command handlers must be thread-safe with 4 worker threads\n- Projection rebuild must not block queries\n- Binary format must support forward/backward compatibility\n- Error handling for corrupted journal recovery\n- Memory-mapped I/O for shared segments\n- Zero-copy where possible for performance\n\nDeliverables:\n1. Command processing pipeline with journaling\n2. Projection engine with shared memory management\n3. Query dispatcher with read-your-writes consistency\n4. Schema evolution system with versioned binary format\n5. Integration with libevent for network I/O\n6. Stress test showing 200 cmd/s + 1000 q/s sustained\n\nAssume x86_64 Linux, pthreads, atomic operations. No high-level frameworks."
messages = [
{"role": "user", "content": prompt},
]
# Process input
inputs = processor.apply_chat_template(
messages,
tokenize=True,
return_dict=True,
return_tensors="pt",
add_generation_prompt=True,
reasoning_strength="high",
).to(model.device)
input_len = inputs["input_ids"].shape[-1]
# Generate output
outputs = model.generate(**inputs)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
print(response)
Esper 4 is created by Valiant Labs.
Check out our HuggingFace page to see all of our models!
We care about open source. For everyone to use.