Fidel2.0-1B

Fidel 2.0 is a compact competitive coding model from 4E-AI, combining Mamba3 MIMO sequence modeling, attention, and a latent mixture-of-experts architecture. This release brings its backbone, coding adaptation, and conversational components together in one model.

The release focuses on competitive programming: interpreting problem statements, reasoning about algorithms and data structures, writing solutions, and revising answers with execution feedback. The weights and tokenizer are published under Apache-2.0 for experimentation, application development, and further adaptation.

This repository contains the complete native SafeTensors weights, original tokenizer, and model configuration.

Fidel Highlights

• Competitive coding focus: Designed for programming problems, algorithmic solutions, and correction from execution feedback.
• Hybrid architecture: Eight Mamba3 MIMO blocks and one attention block combine state-space sequence modeling with attention.
• Latent mixture of experts: Each block routes tokens to two of sixteen experts in a 512-dimensional latent space, alongside a shared feed-forward path.
• Unified weights: Backbone weights, coding adapters, prompt conditioning, and conversational components are included together. No separate adapter download is required.
• Original numerical precision: Every tensor retains its saved data type and values. The release does not apply an additional quantization or precision conversion.
• Conversation formatting: The original tokenizer, special tokens, and chat template are included together.
• Open customization: Apache-2.0 permits use, modification, and redistribution, subject to its terms.

Model Overview

Property	Configuration
Developer	4E-AI
Model type	Text-only autoregressive language model
Primary focus	Competitive coding and algorithmic problem solving
Architecture	Hybrid Mamba3 MIMO, attention, and latent MoE
Model family size	1B
Stored floating-point elements	1,402,955,496
Hidden dimension	1,536
Number of backbone layers	9
Mixer layout	8 Mamba3 MIMO blocks and 1 attention block
Query / key-value heads	16 / 4
Attention head dimension	96
Mamba heads / head dimension	48 / 64
Mamba state dimension / MIMO rank	64 / 4
Experts per MoE layer / selected per token	16 / 2
Latent expert dimension	512
Expert intermediate dimension	2,048
Shared feed-forward intermediate dimension	6,144
Vocabulary size	131,072
Embedding and output head	Separate weight matrices
Combined input/output context limit	8,192 tokens
Weight format	SafeTensors
Stored data types	bfloat16, bool, float32
Weight file size	5.37 GB
License	Apache-2.0

1B is the model family designation. The stored element count includes the full model and persistent floating-point buffers. Expert routing applies to the routed experts; embeddings, shared paths, and other components also contribute to computation.

Fidel Capabilities

Competitive programming. Fidel is intended for tasks involving problem statements, constraints, algorithms, data structures, and complete program solutions. Generated code should be executed and checked against the intended behavior.

Revision from feedback. An application can provide execution results or other feedback in the conversation and ask for a replacement answer. Feedback must come from the application; the model does not execute code on its own.

Conversational prompting. The included chat template supports formatting instructions, examples, and conversation history. Clear requests and relevant context help define the intended task.

Further adaptation. The complete model weights provide a starting point for domain-specific experimentation with a compatible Fidel workflow. Evaluate adapted models on representative data before relying on their outputs.

Fidel accepts text inputs. Image, audio, video, web browsing, and tool execution require separate application components.

Repository Contents

File	Contents
model.safetensors	Complete native model tensors with their original names, data types, and values
model_config.json	Structural model configuration and saved numerical recipe settings
weights_manifest.json	Tensor names, shapes, data types, sizes, and integrity hashes
tokenizer.json	Original tokenizer vocabulary and encoding rules
tokenizer_config.json	Tokenizer configuration and conversation formatting
special_tokens_map.json	Special-token definitions
chat_template.jinja	Original chat template
LICENSE / NOTICE	License and attribution

Usage Notes

• Keep the supplied tokenizer and chat template with the weights. Changes to tokenization or formatting can change model behavior.
• The 8,192-token limit covers the combined prompt, conversation history, and generated answer. It is not a guarantee of reliable behavior throughout that length.
• Longer answers and repeated revisions can require substantially more compute.
• Generated explanations, self-reported checks, and code can be incorrect. Validate them against real sources or execution results.

License

Fidel weights and original Fidel code are licensed under Apache-2.0. Commercial use, modification, and redistribution are permitted subject to its terms. Third-party components retain their respective licenses; see NOTICE.