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This software is copyright 2026-present Prism ML, Inc. It is available under the Apache 2.0 license.
If you publicly deploy or redistribute this software, we would appreciate attribution such as: “Created using Bonsai by Prism ML."
This software is built from Qwen3-8B, Copyright 2024 Alibaba Cloud, which is available under the Apache 2.0 License: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE

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---
license: apache-2.0
tags:
- prismml
- bonsai
---
# Bonsai-8B — Unpacked FP16 Safetensors
FP16 safetensors (HuggingFace format) of the 1-bit Bonsai-8B model. This repo exists for users who want to run Bonsai with stock HuggingFace tooling or frameworks that don't yet support 1-bit weights natively. The 1-bit kernels are currently in our forks of [MLX](https://github.com/PrismML-Eng/mlx) and [llama.cpp](https://github.com/PrismML-Eng/llama.cpp) — once they land upstream, this unpacked version will no longer be needed.
> **We strongly recommend using the native 1-bit models instead.** The 1-bit format is where all the benefits of Bonsai come from — up to 14x memory reduction, 6x faster inference, and 5x lower energy per token. This unpacked FP16 version is full-size and does not provide any of those advantages.
For the optimized 1-bit release models (recommended):
- **[Bonsai-8B GGUF Q1_0](https://huggingface.co/prism-ml/Bonsai-8B-gguf)** — 1-bit GGUF for llama.cpp (CUDA, Metal, CPU)
- **[Bonsai-8B MLX 1-bit](https://huggingface.co/prism-ml/Bonsai-8B-mlx-1bit)** — 1-bit MLX for Apple Silicon

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{
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if 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[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 message.content is string 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.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- 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 }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' }}
{%- endif %}

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"tie_encoder_decoder": false,
"architectures": [
"Qwen3ForCausalLM"
],
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"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"label2id": {
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"LABEL_1": 1
},
"task_specific_params": null,
"problem_type": null,
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"prefix": null,
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"max_length": 20,
"min_length": 0,
"do_sample": false,
"early_stopping": false,
"num_beams": 1,
"temperature": 1.0,
"top_k": 50,
"top_p": 1.0,
"typical_p": 1.0,
"repetition_penalty": 1.0,
"length_penalty": 1.0,
"no_repeat_ngram_size": 0,
"encoder_no_repeat_ngram_size": 0,
"bad_words_ids": null,
"num_return_sequences": 1,
"output_scores": false,
"return_dict_in_generate": false,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"remove_invalid_values": false,
"exponential_decay_length_penalty": null,
"suppress_tokens": null,
"begin_suppress_tokens": [
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151645
],
"num_beam_groups": 1,
"diversity_penalty": 0.0,
"_name_or_path": "",
"transformers_version": "4.57.6",
"no_bias": true,
"tf_legacy_loss": false,
"use_bfloat16": false,
"model_type": "qwen3",
"output_attentions": false
}

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{
"_from_model_config": true,
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"pad_token_id": 151643,
"transformers_version": "4.57.0",
"begin_suppress_tokens": [
151645
],
"temperature": 0.5,
"top_p": 0.85,
"top_k": 20,
"min_p": 0.0,
"presence_penalty": 0.0,
"repetition_penalty": 1.0,
"do_sample": true
}

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
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],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
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}
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
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"normalized": false,
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"single_word": false,
"special": true
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},
"151653": {
"content": "<|vision_end|>",
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},
"151654": {
"content": "<|vision_pad|>",
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},
"151655": {
"content": "<|image_pad|>",
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},
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"content": "<|video_pad|>",
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},
"151657": {
"content": "<tool_call>",
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},
"151658": {
"content": "</tool_call>",
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"special": false
},
"151659": {
"content": "<|fim_prefix|>",
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},
"151660": {
"content": "<|fim_middle|>",
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},
"151661": {
"content": "<|fim_suffix|>",
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},
"151662": {
"content": "<|fim_pad|>",
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},
"151663": {
"content": "<|repo_name|>",
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},
"151664": {
"content": "<|file_sep|>",
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},
"151665": {
"content": "<tool_response>",
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},
"151666": {
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},
"151667": {
"content": "<think>",
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},
"151668": {
"content": "</think>",
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}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
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"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "Qwen2Tokenizer",
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}

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