No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from ibm-granite/granite-switch-4.1-8b-preview
All models by ibm-granite →Model Summary: Granite Switch 4.1 8B Preview is a modular LLM built on IBM Granite 4.1 8B with embedded adapters from the Granite Libraries collection. A single checkpoint supports multiple specialized capabilities — RAG, safety, explainability, and more — that are activated on demand via control tokens in the chat template.
For full details on model composition and adapter configuration, see BUILD.md [blocked].
ibm-granite/granite-4.1-8b (8B params, 128K context)Granite Switch is also available in granite-switch-4.1-3b-preview and granite-switch-4.1-30b-preview.
Motivation: Traditional multi-task LLM deployments require either separate model copies per capability (multiplying memory and compute) or weight merging that permanently blends adapters and destroys task specialization. Granite Switch takes a different approach: independently trained activated LoRA adapters are embedded in a single checkpoint and dynamically selected at inference time via control tokens. KV cache normalization ensures adapters share no internal KV cache state as each adapter sees prior tokens only through the base model's representation. That way, adapters can build on each other's outputs, but never through another adapter's cached activations. This allows adapters to be developed independently and composed without accuracy loss. This makes it possible to implement LLM capabilities very efficiently and very accurately.
Granite Switch is best used with Mellea.
ibm-granite/granitelib-core-r1.0)Adapters for context attribution, requirements validation, and uncertainty estimation.
ibm-granite/granitelib-rag-r1.0)Adapters for retrieval-augmented generation pipelines.
ibm-granite/granitelib-guardian-r1.0)Adapters for safety, factuality, and policy compliance.
Mellea is the preferred way to run Granite Switch adapters in applications. It standardizes the interface for building with adapters like answerability checking, hallucination detection, requirement checker and harmful language detection easily and reliably. Constrained decoding and input/output pre-processing are handled automatically, improving accuracy and reliability. When running Granite Switch models through Mellea, embedded adapters function as high-level API calls. This allows you to use direct operations instead of raw prompt engineering.
See the mellea examples/ directory for more examples, including manual adapter loading.
The following examples demonstrate low-level adapter invocation via the HuggingFace and vLLM backends directly. Check Granite Switch For additional tutorials.
Start the OpenAI-compatible server:
Call adapters via the API:
Or with curl:
Model Artifacts:
Requirements:
Granite Switch uses coarse-grained expert switching — one adapter is active across all layers for a contiguous span of tokens. A lightweight switch layer (standard attention) detects control tokens in the input and produces per-position adapter indices that tell every decoder layer which LoRA weights to apply.
Key properties:
Base Model
Adapter Activation
Each adapter is activated by passing its name to the chat template using an argument. The template inserts the appropriate control token automatically — callers just pass adapter_name.
This model inherits the safety profile of the base Granite 4.1 model. The Guardian Library adapters (guardian core, factuality detection/correction, policy guardrails) provide additional safety layers but are not a substitute for application-level safety testing. Deployers should:
The model.sig file contains a signature over all model artifacts to ensure integrity and provenance.
To verify the integrity of a downloaded adapter, use the model-signing tool:
The "Verification succeeded" message confirms that the model has not been tampered with after release.
Granite Switch has an Apache-2.0 license, as found in the LICENSE file.
See also: Activated LoRA: Fine-tuned LLMs for Intrinsics (NeurIPS 2025).
git clone https://github.com/generative-computing/granite-switch.gitcd granite-switch
# Pick what you need:pip install -e ".[compose]" # Compose models with adapterspip install -e ".[hf]" # HuggingFace inferencepip install -e ".[vllm]" # vLLM inferencepip install -e ".[dev]" # Everythingpip install melleafrom mellea.backends.openai import OpenAIBackendfrom mellea.formatters import TemplateFormatterfrom mellea.stdlib.components import Document, Messagefrom mellea.stdlib.components.intrinsic import ragfrom mellea.stdlib.context import ChatContext
SWITCH_MODEL_ID = "ibm-granite/granite-switch-4.1-8b-preview"
backend = OpenAIBackend( model_id=SWITCH_MODEL_ID, formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), base_url="http://localhost:8000/v1", api_key="EMPTY", load_embedded_adapters=True,)
context = ChatContext().add(Message("assistant", "Hello there, how can I help you?"))question = "What is the square root of 4?"documents = [Document("The square root of 4 is 2.")]
result = rag.check_answerability(question, documents, context, backend)print(f"Answerability: {result}")from mellea.stdlib.components.intrinsic import core
context = ChatContext().add( Message("user", "Invite for an IBM office party.")).add( Message("assistant", "Dear Team, you are cordially invited to a team social..."))
result = core.requirement_check(context, backend, requirement="Use a professional tone.")print(f"Requirements Satisfied: {result}") # float between 0.0 and 1.0from mellea.stdlib.components.intrinsic import guardian
context = ChatContext().add( Message("user", "How can I hack my friend's email?"))
score = guardian.guardian_check(context, backend, criteria="harm", target_role="user")verdict = "Risk detected" if score >= 0.5 else "Safe"print(f"Score: {score:.4f} ({verdict})")import granite_switch.hf # Register the model architecture
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-switch-4.1-8b-preview", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-switch-4.1-8b-preview")messages = [ {"role": "assistant", "content": "Hello there, how can I help you?"}, {"role": "user", "content": "What is the square root of 4?"},]documents = [{"doc_id": "1", "text": "The square root of 4 is 2."}]
prompt = tokenizer.apply_chat_template( messages, documents=documents, adapter_name="answerability", # activate the answerability adapter add_generation_prompt=True, tokenize=False,)
outputs = model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device))print(tokenizer.decode(outputs[0], skip_special_tokens=True))# => "answerable"prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False,)pip install -e ".[vllm]"
python -m vllm.entrypoints.openai.api_server \ --model ibm-granite/granite-switch-4.1-8b-preview \ --port 8000from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
response = client.chat.completions.create( model="ibm-granite/granite-switch-4.1-8b-preview", messages=[ {"role": "assistant", "content": "Hello there, how can I help you?"}, {"role": "user", "content": "What is the square root of 4?"}, ], extra_body={ "documents": [{"doc_id": "1", "text": "The square root of 4 is 2."}], "chat_template_kwargs": {"adapter_name": "answerability"}, }, max_completion_tokens=6,)print(response.choices[0].message.content)# => "answerable"curl -s http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "ibm-granite/granite-switch-4.1-8b-preview", "messages": [ {"role": "assistant", "content": "Hello there, how can I help you?"}, {"role": "user", "content": "What is the square root of 4?"} ], "documents": [{"doc_id": "1", "text": "The square root of 4 is 2."}], "chat_template_kwargs": {"adapter_name": "answerability"}, "max_completion_tokens": 6 }'Input Tokens: ["Tell", "me", "about", "<|answerability|>", ...] │ ┌───────────────▼───────────────┐ │ SWITCH LAYER │ │ Detects control │ │ tokens → indices │ └───────────────┬───────────────┘ │ adapter_indices: [0, 0, 0, 4, 4, ...] │ ┌───────────────▼───────────────┐ │ DECODER LAYERS │ │ Base weights + │ │ LoRA[index] │ └───────────────┬───────────────┘ │ ┌───────────────▼───────────────┐ │ LM HEAD │ └───────────────────────────────┘# First obrain the model weightshf download ibm-granite/granite-switch-4.1-8b-preview --local-dir granite-switch-4.1-8b-preview
# Install the model signing verification toolpip install 'model-signing==v1.1.1'
# Verify all artifacts in an adapter's lora/ directorymodel_signing verify sigstore \ --signature granite-switch-4.1-8b-preview/model.sig \ --ignore-git-paths \ --ignore-paths granite-switch-4.1-8b-preview/README.md \ --identity Granite-sign@ibm.com \ --identity_provider https://sigstore.verify.ibm.com/oauth2 \ granite-switch-4.1-8b-preview@software{granite_switch, title = {Granite Switch: Coarse-Grained Expert Switching for LLMs}, author = {IBM Research}, year = {2026}, url = {https://github.com/generative-computing/granite-switch}}