alt.freeweights.granite / granite-3.2-2b-instruct
From: ibm-granite <huggingface.co/ibm-granite>Newsgroups: alt.freeweights.graniteSubject:

granite-3.2-2b-instruct

Date: 2026-11-15Commit: 641593cSigned: 2DC28CC7B26A283B ✓

Text generation model with 2.5B parameters. Safetensors weights, 5.07 GB in all. Published by ibm-granite under Apache-2.0.

2.5B params5.07GBgranite-3.2-2b-instructsafetensorspermissive
FactsParams2.5BSize5.07 GBFormatssafetensorsTasktext generationLicenseApache-2.0From sourcepermissive
$ fw get ibm-granite/granite-3.2-2b-instruct
# manifest sha256 8bb8b32a6662… · 5.07 GB · 13 files
$ fw verify ./granite-3.2-2b-instruct ibm-granite/granite-3.2-2b-instruct
From sourceHosted (signed)ibm-granite/granite-3.2-2b-instructLast check: 2026-11-15 02:50 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Fine-tune of granite-3.1-2b-instruct.

Works with

Overview

Size
5.07 GB
Weights
5.07 GB (safetensors)
Parameters
2.5B
Formats
safetensors
Task
text generation
License
Apache-2.0 (permissive)
Files
13, each with a sha256
Publisher
ibm-granite
Added
2026-11-15
Section
alt.freeweights.granite
Lineage

Original release from the publisher.

Base model: ibm-granite/granite-3.1-2b-instruct

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Model card

From the publisher at commit 641593c. Shown as plain text with links; images open at the publisher.

Granite-3.2-2B-Instruct#

Model Summary: Granite-3.2-2B-Instruct is an 2-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of Granite-3.1-2B-Instruct, it has been trained using a mix of permissively licensed open-source datasets and internally generated synthetic data designed for reasoning tasks. The model allows controllability of its thinking capability, ensuring it is applied only when required.

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may finetune this Granite model for languages beyond these 12 languages.

Intended Use: This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.

Capabilities

  • Thinking
  • Summarization
  • Text classification
  • Text extraction
  • Question-answering
  • Retrieval Augmented Generation (RAG)
  • Code related tasks
  • Function-calling tasks
  • Multilingual dialog use cases
  • Long-context tasks including long document/meeting summarization, long document QA, etc.

Generation: This is a simple example of how to use Granite-3.2-2B-Instruct model.

Install the following libraries:

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Then, copy the snippet from the section that is relevant for your use case.

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import torch

model_path="ibm-granite/granite-3.2-2b-instruct"
device="cuda"
model = AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map=device,
        torch_dtype=torch.bfloat16,
    )
tokenizer = AutoTokenizer.from_pretrained(
        model_path
)

conv = [{"role": "user", "content":"You have 10 liters of a 30% acid solution. How many liters of a 70% acid solution must be added to achieve a 50% acid mixture?"}]

input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)

set_seed(42)
output = model.generate(
    **input_ids,
    max_new_tokens=8192,
)

prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
print(prediction)

Example Outputs

  • thinking=True
Here is my thought process:

1. First, let's understand the composition of the initial 10 liters of 30% acid solution:
   - Acid content = 30% of 10 liters = 0.30 * 10 = 3 liters

2. We want to end up with a 50% acid solution. Let's denote the volume of the 70% acid solution to be added as 'x' liters.

3. The amount of acid in the final mixture (50% solution) should equal the initial acid content plus the acid from the added 70% solution.
   - Total acid in final mixture = 0.50 * (10 + x)

4. The equation representing the total acid content is:
   3 (from initial solution) + 0.70 * x (from added solution) = 0.50 * (10 + x)

5. Now, we solve for 'x':
   3 + 0.70x = 5 + 0.50x
   0.70x - 0.50x = 5 - 3
   0.20x = 2
   x = 2 / 0.20
   x = 10 liters

6. Therefore, 10 liters of a 70% acid solution must be added to the initial 10 liters of 30% acid solution to achieve a 50% acid mixture.

Here is my response:

To solve this problem, we need to determine how much of a 70% acid solution must be added to a 10-liter solution that is already 30% acid to create a new mixture that is 50% acid. Here's how to calculate it:

1. **Initial Acid Content**:
   - The initial 10-liter solution contains 3 liters of acid (since 30% of 10 liters).

2. **Desired Final Acid Content**:
   - We want the final mixture to be 50% acid.

3. **Equation Setup**:
   - Let 'x' be the volume (in liters) of the 70% acid solution to be added.
   - The equation representing the total acid content in the final mixture (which is 10 liters from the initial solution plus 'x' liters of the added solution) should equal 50% of the total volume (10 + x).
   - This gives us: 3 (acid from initial solution) + 0.70x (acid from added solution) = 0.50(10 + x)

4. **Solving the Equation**:
   - First, simplify the right side of the equation: 0.50(10 + x) = 5 + 0.50x
   - Now, the equation is: 3 + 0.70x = 5 + 0.50x
   - Subtract 0.50x from both sides: 0.20x = 2
   - Divide both sides by 0.20: x = 2 / 0.20
   - x = 10 liters

5. **Conclusion**:
   - To achieve a 50% acid mixture from the initial 30% solution, you need to add 10 liters of a 70% acid solution.

So, the final answer is that 10 liters of a 70% acid solution must be added to the initial 10 liters of 30% acid solution.
  • thinking=False
To solve this problem, we'll use the concept of volume and concentration. Let's denote the amount of 70% acid solution we need to add as "x" liters.

First, let's find out how much acid is in the initial 10-liter solution:

Acid in initial solution = 30% of 10 liters = 0.30 * 10 = 3 liters

Now, we want to end up with a 50% acid solution in a total volume of (10 + x) liters. Let's denote the final volume as V.

Final acid concentration = 50%
Final acid amount = 50% of V = 0.50 * V

We know the initial acid amount and the final acid amount, so we can set up an equation:

Initial acid amount + Acid from added solution = Final acid amount
3 liters + (70% of x) = 0.50 * (10 + x)

Now, let's solve for x:

0.70x + 3 = 0.50 * 10 + 0.50x
0.70x - 0.50x = 0.50 * 10 - 3
0.20x = 5 - 3
0.20x = 2
x = 2 / 0.20
x = 10 liters

So, you need to add 10 liters of a 70% acid solution to the initial 10-liter 30% acid solution to achieve a 50% acid mixture.

Evaluation Results:

| Models | ArenaHard | Alpaca-Eval-2 | MMLU | PopQA | TruthfulQA | BigBenchHard | DROP | GSM8K | HumanEval | HumanEval+ | IFEval | AttaQ

| Llama-3.1-8B-Instruct | 36.43 | 27.22 | 69.15 | 28.79 | 52.79 | 72.66 | 61.48 | 83.24 | 85.32 | 80.15 | 79.10 | 83.43

| DeepSeek-R1-Distill-Llama-8B | 17.17 | 21.85 | 45.80 | 13.25 | 47.43 | 65.71 | 44.46 | 72.18 | 67.54 | 62.91 | 66.50 | 42.87

| Qwen-2.5-7B-Instruct | 25.44 | 30.34 | 74.30 | 18.12 | 63.06 | 70.40 | 54.71 | 84.46 | 93.35 | 89.91 | 74.90 | 81.90

| DeepSeek-R1-Distill-Qwen-7B | 10.36 | 15.35 | 50.72 | 9.94 | 47.14 | 65.04 | 42.76 | 78.47 | 79.89 | 78.43 | 59.10 | 42.45

| Granite-3.1-8B-Instruct | 37.58 | 30.34 | 66.77 | 28.7 | 65.84 | 68.55 | 50.78 | 79.15 | 89.63 | 85.79 | 73.20 | 85.73

| Granite-3.1-2B-Instruct | 23.3 | 27.17 | 57.11 | 20.55 | 59.79 | 54.46 | 18.68 | 67.55 | 79.45 | 75.26 | 63.59 | 84.7

| Granite-3.2-8B-Instruct | 55.25 | 61.19 | 66.79 | 28.04 | 66.92 | 64.77 | 50.95 | 81.65 | 89.35 | 85.72 | 74.31 | 85.42

| Granite-3.2-2B-Instruct | 24.86 | 34.51 | 57.18 | 20.56 | 59.8 | 52.27 | 21.12 | 67.02 | 80.13 | 73.39 | 61.55 | 83.23

Training Data: Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2) internal synthetically generated data targeted to enhance reasoning capabilites.

Infrastructure: We train Granite-3.2-2B-Instruct using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.

Ethical Considerations and Limitations: Granite-3.2-2B-Instruct builds upon Granite-3.1-2B-Instruct, leveraging both permissively licensed open-source and select proprietary data for enhanced performance. Since it inherits its foundation from the previous model, all ethical considerations and limitations applicable to Granite-3.1-2B-Instruct remain relevant.

Resources

Files

13 files · 5.07 GBWeights 5.07 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 13 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md18.4 KB87b19f8c6d1a7c7c88926b157e90e233b5a62712612b4cc292c5c44bcd3ab4f0
added_tokens.json87 B081bdeaf76fc5463cc2e20fbadf17ad9eb569bb5343b120cc64b98840754f96c
config.json786 B5832d470c0ebb05cb0760fe05f80fa3e67ba42ba2d0a549bd36c6807f8d18672
generation_config.json137 B7221a61ef9b4e8787343a1bb5f2bfe3d28a078cd61f403b68fbb267f93e74391
merges.txt442 KB303127a244b0078878156c17229f36d11b7a3a3f8e47b7cfdbb304ff46be5030
model-00001-of-00002.safetensors5.00 GB5720a4536051b0cd90ccbd9b3d8a97283f9fb07a4c4066969f88b0d9d70e5872
model-00002-of-00002.safetensors67.1 MB73833730ea9eebdff5f79d6defd78a3cc05aeaa1d18fdd81d0db2f5a80ab5756
model.safetensors.index.json29.8 KB75f910f2ef1db766353d67cfc4d8d246dea9c817ec360d62ae05def22d90f938
special_tokens_map.json701 B596d752bf46f5cace1f6826b52ed7d913347a4eea0ecce8ab2f869471ca40369
tokenizer.json3.48 MBed944c0b3d71e2d651a5ba0f8e7b350faff30031c8e3353de70b12b9c1bf5654
tokenizer_config.json8.88 KBfdc617feda7907bddc63907c2e922b1cbd677925b2068403cecc29c349a4725c
vocab.json777 KB80ab859339a2525fdfbda14bc39df02dffb824aefdaf86426217bbb146d17e01
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
641593c3b2 pinned now2026-11-15 02:50 UTC13 files · 5.07 GB

Use it

$ fw get ibm-granite/granite-3.2-2b-instruct
# manifest sha256 8bb8b32a6662… · 5.07 GB · 13 files
$ fw verify ./granite-3.2-2b-instruct ibm-granite/granite-3.2-2b-instruct
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/ibm-granite/granite-3.2-2b-instruct.json --dir ./granite-3.2-2b-instruct $ python3 fw.py verify ./granite-3.2-2b-instruct ./ibm-granite__granite-3.2-2b-instruct.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download ibm-granite/granite-3.2-2b-instruct --revision 641593c3b25bec0b1efe9f0f7d7a67f7243f86a3 --local-dir ./granite-3.2-2b-instruct
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("ibm-granite/granite-3.2-2b-instruct", revision="641593c3b25bec0b1efe9f0f7d7a67f7243f86a3") model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3.2-2b-instruct", revision="641593c3b25bec0b1efe9f0f7d7a67f7243f86a3", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve ibm-granite/granite-3.2-2b-instruct --revision 641593c3b25bec0b1efe9f0f7d7a67f7243f86a3

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
87b19f8c6d1a7c7c88926b157e90e233b5a62712612b4cc292c5c44bcd3ab4f0
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: ibm-granite <huggingface.co/ibm-granite>Newsgroups: alt.freeweights.graniteSubject: granite-3.2-2b-instructDate: 2026-11-15Commit: 641593cSigned: 2DC28CC7B26A283B ✓
Upstream
ibm-granite/granite-3.2-2b-instruct
Pinned commit
641593c3b25bec0b1efe9f0f7d7a67f7243f86a3
Manifest
granite-3.2-2b-instruct.json · .minisig
Manifest sha256
8bb8b32a6662994bed7876a682ff239c05344c96c57dde6a8e75d824cf713547
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:50 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/ibm-granite/granite-3.2-2b-instruct.json $ curl -O https://freeweights.allternit.com/manifests/ibm-granite/granite-3.2-2b-instruct.json.minisig $ minisign -Vm granite-3.2-2b-instruct.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

ibm-granite/granite-3.1-2b-instruct · this model is a finetune of it

3 models in the catalog name this one as their parent.

Quantizations
3
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download ibm-granite/granite-3.2-2b-instruct
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. granite-3.2-2b-instruct.json. Provenance. Takedown history: none.

Variants

3 quantized or adapter builds of granite-3.2-2b-instruct. Each has its own license check and page.

From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.Edu.seed1
From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.Edu.seed2
From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.Edu.seed3
From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.seed1
From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.seed2
From: ibm-granite <huggingface.co/ibm-granite>Subject: GneissWeb.7B_ablation_model_on_350B_FineWeb.seed3