Introduction
AI21’s Jamba Reasoning 3B is a top-performing reasoning model that packs leading scores on intelligence benchmarks and highly-efficient processing into a compact 3B build.
Read the full blog post
here.
Key Advantages
Fast: Optimized for efficient sequence processing
The hybrid design combines Transformer attention with Mamba (a state-space model). Mamba layers are more efficient for sequence processing, while attention layers capture complex dependencies. This mix reduces memory overhead, improves throughput, and makes the model run smoothly on laptops, GPUs, and even mobile devices, while maintainig impressive quality.
Smart: Leading intelligence scores
The model outperforms competitors, such as Gemma 3 4B, Llama 3.2 3B, and Granite 4.0 Micro, on a combined intelligence score that averages 6 standard benchmarks.
Scalable: Handles very long contexts
Unlike most compact models, Jamba Reasoning 3B supports extremely long contexts. Mamba layers allow the model to process inputs without storing massive attention caches, so it scales to 256K tokens while keeping inference practical. This makes it suitable for edge deployment as well as datacenter workloads.
Model Details
- Number of Parameters: 3B
- Number of Layers: 28 (26 Mamba, 2 Attention)
- Number of Attention Heads: 20 MQA (20 for Q, 1 for KV)
- Vocabulary Size: 64K
- Context Length: 256k
- Architecture: Hybrid Transformer–Mamba with efficient attention and long-context support
- Developed by: AI21
- Supported languages: English, Spanish, French, Portuguese, Italian, Dutch, German, Arabic and Hebrew
- Intelligence benchmark results:
| MMLU-Pro | Humanity’s Last Exam | IFBench |
|---|
| DeepSeek R1 Distill Qwen 1.5B | 27.0% | 3.3% | 13.0% |
| Phi-4 mini | 47.0% | 4.2% | 21.0% |
| Granite 4.0 Micro | 44.7% | 5.1% | 24.8% |
| Llama 3.2 3B | 35.0% | 5.2% | 26.0% |
| Gemma 3 4B | 42.0% | 5.2% | 28.0% |
| Qwen 3 1.7B | 57.0% | 4.8% | 27.0% |
| Qwen 3 4B | 70% | 5.1% | 33% |
| Jamba Reasoning 3B | 61.0% | 6.0% | 52.0% |
Quickstart
You can run Jamba Reasoning 3B on your own machine using popular lightweight runtimes. This makes it possible to experiment with long-context reasoning without relying on cloud infrastructure.
- Supported runtimes: llama.cpp, LM Studio, and Ollama.
- Quantizations: Multiple quantization levels are provided to shrink the model size.
- Full precision FP16 GGUF - 6.4 GB
- 4 bit quantization using Q4-K-M GGUF - 1.93 GB
Run the model with llama.cpp
llama.cpp Python SDK
1pip install llama-cpp-python
2pip install huggingface_hub
1from llama_cpp import Llama
2from huggingface_hub import hf_hub_download
3
4# Download from HF
5model_path = hf_hub_download(
6 repo_id="ai21labs/AI21-Jamba-Reasoning-3B-GGUF",
7 filename="jamba-reasoning-3b-Q4_K_M.gguf",
8 token="<HF token>"
9)
10
11llm = Llama(
12 model_path=model_path,
13 n_ctx=128000,
14 n_threads=10, # CPU threads
15 n_gpu_layers=-1, # -1 = all layers on GPU (Metal/CUDA if available)
16 flash_attn=True,
17)
18
19prompt = """
20You are analyzing a stream of customer support tickets to decide which ones require escalation.
21
22Ticket 1: "The new update caused our app to crash whenever users upload a file larger than 50MB."
23Ticket 2: "I can't log in because I forgot my password."
24Ticket 3: "The billing page is missing the new enterprise pricing option."
25
26Classify each ticket as 'Critical', 'Medium', or 'Low' priority and explain your reasoning."""
27res = llm(
28 prompt,
29 max_tokens=8192,
30 temperature=0.6,
31)
32
33print(f"\n\nResponse: {res['choices'][0]['text']}\n\n")
llama.cpp server
1git clone https://github.com/ggml-org/llama.cpp
2cd llama.cpp
3cmake -B build
4cmake --build build --config Release
Start llama.cpp server with Jamba-Reasoning-3B gguf:
1./build/bin/llama-server --jinja \
2--hf-repo ai21labs/AI21-Jamba-Reasoning-3B-GGUF \
3--hf-file jamba-reasoning-3b-Q4_K_M.gguf \
4-ngl -1 \
5--host 127.0.0.1 \
6--port 8000
Quick sanity test using curl:
1curl --location 'http://127.0.0.1:8000/v1/chat/completions' \
2--header 'Content-Type: application/json' \
3--data '{
4 "model": "jamba-reasoning-3b",
5 "messages": [
6 {
7 "role": "user",
8 "content": "You are analyzing customer support tickets to decide which need escalation.\nTicket 1: '\''App crashes when uploading files >50MB.'\''\nTicket 2: '\''Forgot password, can’t log in.'\''\nTicket 3: '\''Billing page missing enterprise pricing.'\''\nClassify each ticket as Critical, Medium, or Low and explain your reasoning.\n"
9 }
10 ],
11 "max_tokens": 8192,
12 "temperature": 0.6
13}'
Run the model with vLLM
[!NOTE]
Please reference the base model's model card
here.
Training Details
We trained the model in multiple stages, each designed to strengthen reasoning and long-context performance. The process began with large-scale pre-training on a diverse corpus of natural documents. We then mid-trained on ~0.5T tokens of math and code, while extending the context length to 32K tokens. During this stage we also applied a
Mamba-specific long-context method, which we found to significantly improve long-context abilities.
To improve reasoning, tool use, and instruction following, we applied cold-start distillation: supervised fine-tuning with a 32K window and direct preference optimization with a 64K window. Finally, we enhanced reasoning performance further through online reinforcement learning with RLVR, targeting tasks such as code generation, mathematical problem solving, structured output, and information extraction.
Reinforcement “Fine-Tuning”
Full support for training Jamba through VeRL will be available soon. AI21 has introduced several improvements to the VeRL framework (
https://github.com/volcengine/verl), including new capabilities for training hybrid models, and stability improvements for GRPO training. These improvements will soon be available to the open source community.
License
Citation
- Blog post- Read the full blog post here.
