Fidel1.1-1B

[!NOTE] This repository contains the complete Fidel model weights, tokenizer, and custom model implementation for Hugging Face Transformers. For quantized native CPU inference, see Fidel1.1-1B-GGUF.

Fidel 1.1 is a compact text-generation model from 4E-AI, combining Mamba3 MIMO sequence modeling, attention, and a latent mixture-of-experts architecture. It brings the model's backbone, coding adapters, and conversational components together in one package for local inference and application development.

Fidel is available in a complete FP32 Transformers edition and four GGUF storage variants. The weights and original Fidel code are released under Apache-2.0, allowing developers to use, modify, and redistribute them under the license terms.

Fidel Highlights

• 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.
• Complete model package: Backbone weights, coding adaptation, prompt conditioning, and chat components are included in one SafeTensors file. No separate adapter download is required.
• Local execution: The Transformers implementation runs through PyTorch on CPU or compatible CUDA devices without installing the training repository or custom CUDA extensions.
• Multiple deployment sizes: The companion GGUF release offers F16, Q8, Q5, and mixed Q4 files with a dedicated Fidel native CPU runtime.
• Open customization: Apache-2.0 permits building applications, modifying the original code, and developing derivative models, subject to its terms.

Model Overview

The complete package contains approximately 1.403 billion floating-point elements across model weights and buffers, together with Boolean state. 1B is the family designation. MoE selection applies to the routed experts; embeddings, shared paths, and other model components contribute separately to computation.

Fidel Capabilities

Conversational text generation. Fidel accepts chat-formatted text through its included tokenizer template. Developers can build local chat interfaces and experiment with instructions, examples, and conversation history in the prompt.

Code-oriented prompting. Coding adapters are included in the model. Fidel can be prompted with programming questions, source snippets, or code-completion tasks. Execute and test generated code before incorporating it into a project.

Application-specific text workflows. The model can be incorporated into prototypes for drafting, rewriting, summarization, or question answering over supplied text. These are intended application areas to evaluate on your own data, rather than guaranteed performance levels.

Local and offline integration. After downloading the repository and its dependencies, inference can run without a hosted model API. You control the prompts, storage, and application surrounding the model.

Fidel accepts text inputs. Image, audio, video, web browsing, and tool execution require separate application components; they are not provided by this model package.

Local Use and Customization

Choose an execution format

The Transformers weights occupy approximately 5.61 GB on disk. Inference also needs memory for the framework, activations, and temporary tensors. The GGUF page lists file sizes and native runtime instructions.

Adapt behavior with prompts and context

Start with a clear instruction, the relevant source material, and an example of the desired answer format. For a domain-specific application, retrieve a small set of relevant document passages and include them in the user message. This allows you to experiment with your own knowledge sources without modifying model weights.

For conversation, preserve prior messages in the chat-template input and manage their combined token length. Start with short prompts and modest output limits while testing the application. Review generated answers against the supplied sources and validate structured outputs in application code.

Fine-tune for your coding domain

If you want to specialize Fidel for a particular coding segment, the full model weights provide a starting point for further adaptation. Examples include Python data workflows, JavaScript web development, SQL generation, or the conventions of an internal codebase.

Use carefully prepared examples from your target domain and evaluate the adapted model on representative tasks. A Fidel-compatible fine-tuning workflow is required. For customized GGUF deployment, adapt the full-precision model first, then convert and check the resulting model for local use.

Usage Notes

• Use FP32, one unpadded sequence, and one device for Transformers inference.
• Generation uses full-prefix replay with use_cache=False. Longer conversations and outputs increase compute cost.
• The current adapter enforces an 8,192-token combined input/output limit. This is an implementation limit, not a guarantee of reliable behavior at that length.
• The runtime has been exercised on CPU and NVIDIA T4, A10, and H100. Compatibility with other devices depends on their PyTorch support and needs testing.
• Keep the supplied tokenizer and chat template. Automatic tokenizer-regex changes can alter tokenization.
• Reduced-precision Transformers loading, padded batching, multi-device dispatch, and alternative serving engines are not supported by the included integration.
• Generated answers and code can be incorrect. Check them for the needs of your application.

This edition stores FP32 weights. A packed NVIDIA NVFP4 edition is not included here.

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 dependencies retain their respective licenses; see NOTICE.

How to use with Transformers

Use Python 3.11 and the tested torch==2.7.0 / transformers==4.57.6 combination. Install a PyTorch build suitable for your CPU or CUDA environment, followed by:

pip install "transformers==4.57.6" safetensors accelerate

The repository contains custom model code. Review it before enabling trust_remote_code=True; for reproducible applications, set revision to the desired repository commit hash in both loading calls.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "4E-AI/Fidel1.1-1B"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    torch_dtype=torch.float32,
).eval().to(device)

messages = [{"role": "user", "content": "Hello!"}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.inference_mode():
    output = model.generate(
        **inputs, max_new_tokens=32, do_sample=False, use_cache=False,
    )
new_tokens = output[0, inputs.input_ids.shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

For offline use, download the complete repository, then replace repo_id with its local directory. CPU execution uses the same code and is slower. Keep batch size one, avoid padding, retain FP32, and leave caching disabled. Do not use device_map="auto" to split this policy across devices. The GGUF files in the companion repository use a separate native runtime and are not inputs to this Transformers loader.