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---
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
|
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---
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||||
|
||||
# Introduction
|
||||
Jamba2 3B is an ultra-compact open source model designed to bring enterprise-grade reliability to on-device deployments. At just 3B parameters, it runs efficiently on consumer devices—iPhones, Androids, Macs, and PCs—while maintaining the grounding and instruction-following capabilities required for production use.
|
||||
|
||||
Released under Apache 2.0 License with a 256K context window, Jamba2 3B enables developers to build reliable AI applications for edge environments. For more details, read the [full release blog post](https://www.ai21.com/blog/introducing-jamba2/).
|
||||
|
||||
# Key Advantages
|
||||
* **On-device deployment:** Runs efficiently on iPhones, Androids, Macs, and PCs
|
||||
* **Ultra-compact footprint:** 3B parameters enabling edge deployments with minimal resources
|
||||
* **Benchmark leadership:** Excels on IFBench, IFEval, Collie, and FACTS
|
||||
* **256K context window:** Processes long documents and knowledge bases
|
||||
* **Apache 2.0 License:** Fully open source for commercial use
|
||||
* **SSM-Transformer architecture:** Memory-efficient design for resource-constrained environments
|
||||
|
||||
# Evaluation Results
|
||||
Jamba2 3B achieves category-leading performance on instruction following and grounding benchmarks despite its compact size. The model delivers consistent, context-faithful outputs across diverse enterprise tasks including RAG workflows and technical document processing.
|
||||
|
||||
<img src="https://huggingface.co/ai21labs/AI21-Jamba2-3B/resolve/main/assets/Enterprise%20Reliability%20Benchmarks%20for%20Tiny%20Models.png" width="900"/>
|
||||
|
||||
# Training and Evaluation Details
|
||||
Jamba2 models were developed using a comprehensive post-training pipeline starting from Jamba 1.5 pre-training. The models underwent mid-training on 500B carefully curated tokens with increased representation of math, code, high-quality web data, and long documents. A state passing phase optimized the Mamba layers for effective context length generalization. Training continued with cold start supervised fine-tuning to establish instruction-following and reasoning capabilities, followed by DPO optimization.
|
||||
|
||||
The final training stages involved multiple on-policy reinforcement learning phases, progressively moving from short-context verifiable rewards to longer context training with mixed verifiable and model-based rewards. Evaluation focused on two key enterprise reliability signals: instruction-following benchmarks measuring steerability, and grounding benchmarks testing context faithfulness. Human evaluators assessed performance on real-world enterprise tasks using blind, counterbalanced side-by-side comparisons, rating outputs on factuality, style, constraint-adherence, instruction-following, and helpfulness.
|
||||
|
||||
# Quickstart
|
||||
## Run with vLLM
|
||||
Best results require vLLM version **0.10.2** or higher.
|
||||
```
|
||||
vllm serve "ai21labs/AI21-Jamba2-3B" --mamba-ssm-cache-dtype float32 --enable-auto-tool-choice --tool-call-parser hermes --enable-prefix-caching
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||||
```
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||||
## Run with Transformers
|
||||
```
|
||||
pip install transformers>= 4.54.0
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||||
pip install flash-attn --no-build-isolation
|
||||
pip install causal-conv1d>=1.2.0
|
||||
pip install mamba-ssm
|
||||
```
|
||||
```
|
||||
import torch
|
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from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained("ai21labs/AI21-Jamba2-3B",
|
||||
dtype=torch.bfloat16,
|
||||
attn_implementation="flash_attention_2",
|
||||
device_map="auto")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("ai21labs/AI21-Jamba-3B")
|
||||
|
||||
messages = [
|
||||
{"role": "system",
|
||||
"content": "You are an HR Policy Assistant.
|
||||
Answer employee questions using only the provided policy documents.
|
||||
If the answer isn't in the documents, say so clearly.
|
||||
Be concise and cite the specific policy section when possible."
|
||||
},
|
||||
{"role": "user",
|
||||
"content": "Context documents: {retrieved_chunks}.
|
||||
Employee question: {user_question}.
|
||||
Answer:"
|
||||
},
|
||||
]
|
||||
|
||||
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
|
||||
|
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outputs = model.generate(**tokenizer(prompts, return_tensors="pt").to(model.device), do_sample=True, temperature=0.6)
|
||||
|
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
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print(generated_text)
|
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```
|
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BIN
assets/Enterprise Reliability Benchmarks for Tiny Models.png
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assets/Enterprise Reliability Benchmarks for Tiny Models.png
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66
chat_template.jinja
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chat_template.jinja
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{%- if bos_token is defined and bos_token is not none %}{{- bos_token -}}{%- endif %}
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages|length > 0 and messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages|length > 0 and messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
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||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
|
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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||||
{{- '<|im_start|>assistant\n' }}
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||||
{%- endif -%}
|
||||
41
config.json
Normal file
41
config.json
Normal file
@@ -0,0 +1,41 @@
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Normal file
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Normal file
7
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45
special_tokens_map.json
Normal file
45
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Normal file
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|
||||
{
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||||
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3
tokenizer.json
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3
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257
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Normal file
257
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Normal file
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|
||||
"540": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"541": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"542": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": "<|startoftext|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|pad|>",
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizerFast",
|
||||
"unk_token": "<|unk|>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user