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Model: andrevp/Nanbeige4.1-3B-MLX-BF16
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+library_name: mlx
+pipeline_tag: text-generation
+tags:
+- llm
+- nanbeige
+- mlx
+- bf16
+- full-precision
+- reasoning
+- tool-calling
+- apple-silicon
+base_model: Nanbeige/Nanbeige4.1-3B
+---
+
+# Nanbeige4.1-3B-MLX-BF16 (Full Precision)
+
+This is the [Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B) model converted to [MLX](https://github.com/ml-explore/mlx) format in **full BF16 precision** (no quantization) for maximum quality inference on Apple Silicon.
+
+> **Other variants:**
+> - [andrevp/Nanbeige4.1-3B-MLX-4bit](https://huggingface.co/andrevp/Nanbeige4.1-3B-MLX-4bit) — Smallest & fastest (~2.06 GB)
+> - [andrevp/Nanbeige4.1-3B-MLX-8bit](https://huggingface.co/andrevp/Nanbeige4.1-3B-MLX-8bit) — Best balance of quality & speed (~3.91 GB)
+
+## All Variants Compared
+
+### Performance
+
+| Variant | Size | Memory | Prompt Speed | Gen Speed |
+|---------|:----:|:------:|:------------:|:---------:|
+| [4-bit](https://huggingface.co/andrevp/Nanbeige4.1-3B-MLX-4bit) | 2.06 GB | ~2.3 GB | ~279 tok/s | ~103 tok/s |
+| [8-bit](https://huggingface.co/andrevp/Nanbeige4.1-3B-MLX-8bit) | 3.91 GB | ~4.3 GB | ~342 tok/s | ~59 tok/s |
+| **BF16 (this)** | **7.35 GB** | **~8.0 GB** | **~276 tok/s** | **~33 tok/s** |
+
+### Quality Comparison (Head-to-Head, Identical Prompts, temp=0)
+
+All three variants were tested with the same prompts under deterministic settings (temperature=0) to evaluate quality differences:
+
+| Test | 4-bit | 8-bit | BF16 |
+|------|:-----:|:-----:|:----:|
+| Math: 47 * 83 | 3901 | 3901 | 3901 |
+| Logic: "All but 9 die" trick | 9 | 9 | 9 |
+| Code: Binary search | Correct | Correct | Correct |
+| Math: f(x)=2x^2-3x+1, f(5) | 36 | 36 | 36 |
+| Nuanced reasoning: Paper folding | Correct | Correct | Correct |
+| Tool call: BookFlight JSON | **Identical** | **Identical** | **Identical** |
+| AIME-style: 2^100 mod 7 | 2 | 2 | 2 |
+
+**Key findings:**
+- **8-bit vs BF16:** Produced **word-for-word identical** reasoning and answers in the majority of tests. Essentially zero quality loss.
+- **4-bit vs BF16:** Sometimes takes slightly different reasoning paths, but arrives at the **same correct answers**. Tool calling output is **100% identical** across all variants.
+- **Recommendation:** 4-bit is best for speed and memory. 8-bit is the sweet spot for quality. BF16 is for research and benchmarking where exact reproduction matters.
+
+## Original Model
+
+- **Source:** [Nanbeige/Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B)
+- **Developer:** [Nanbeige (BOSS Zhipin)](https://huggingface.co/Nanbeige)
+- **Architecture:** LlamaForCausalLM (4B parameters)
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) (same as the original model)
+
+## Conversion Details
+
+| Property | Value |
+|----------|-------|
+| Precision | BF16 (full precision) |
+| Quantization | None |
+| Model size | ~7.3 GB |
+| Conversion tool | [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms) v0.30.7 |
+
+## Capabilities
+
+This model retains **all capabilities** of the original Nanbeige4.1-3B at full precision:
+
+- **Reasoning/Thinking:** Uses `<think>...</think>` tags for chain-of-thought reasoning
+- **Tool/Function Calling:** Generates structured `<tool_call>...</tool_call>` JSON output
+- **Multi-turn Conversation:** Supports multi-turn chat with context tracking
+- **Multilingual:** Strong performance in both English and Chinese
+- **Code Generation:** Capable of writing and explaining code
+- **Deep-Search Agent:** Supports deep-search tasks with 500+ rounds of tool invocations (using `tokenizer_config_search.json`)
+
+## Quickstart
+
+### Installation
+
+```bash
+pip install mlx-lm
+```
+
+### CLI Usage
+
+```bash
+mlx_lm generate \
+  --model andrevp/Nanbeige4.1-3B-MLX-4bit-BF16 \
+  --prompt "Explain quantum computing in simple terms." \
+  --max-tokens 512 \
+  --temp 0.6 \
+  --top-p 0.95
+```
+
+### Python - Chat
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load("andrevp/Nanbeige4.1-3B-MLX-4bit-BF16")
+sampler = make_sampler(temp=0.6, top_p=0.95)
+
+messages = [
+    {"role": "user", "content": "Which number is bigger, 9.11 or 9.8?"}
+]
+prompt = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+response = generate(
+    model, tokenizer, prompt=prompt, max_tokens=512, sampler=sampler
+)
+print(response)
+```
+
+### Python - Tool Calling
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load("andrevp/Nanbeige4.1-3B-MLX-4bit-BF16")
+sampler = make_sampler(temp=0.6, top_p=0.95)
+
+messages = [
+    {"role": "user", "content": "What is the weather in Tokyo?"}
+]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "SearchWeather",
+            "description": "Find the current weather in a city.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "City name"
+                    }
+                },
+                "required": ["location"]
+            }
+        }
+    }
+]
+prompt = tokenizer.apply_chat_template(
+    messages, tools=tools, add_generation_prompt=True, tokenize=False
+)
+response = generate(
+    model, tokenizer, prompt=prompt, max_tokens=256, sampler=sampler
+)
+print(response)
+```
+
+### Python - Multi-turn with Tool Responses
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load("andrevp/Nanbeige4.1-3B-MLX-4bit-BF16")
+sampler = make_sampler(temp=0.6, top_p=0.95)
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "SearchWeather",
+            "description": "Find the current weather in a city.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string", "description": "City name"}
+                },
+                "required": ["location"]
+            }
+        }
+    }
+]
+
+messages = [
+    {"role": "user", "content": "What's the weather like in Paris?"},
+    {"role": "assistant", "content": "", "tool_calls": [
+        {"function": {"name": "SearchWeather", "arguments": '{"location": "Paris"}'}}
+    ]},
+    {"role": "tool", "content": '{"temperature": "18°C", "condition": "Partly cloudy"}'}
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages, tools=tools, add_generation_prompt=True, tokenize=False
+)
+response = generate(
+    model, tokenizer, prompt=prompt, max_tokens=300, sampler=sampler
+)
+print(response)
+```
+
+## Recommended Inference Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Temperature | 0.6 |
+| Top-p | 0.95 |
+| Repeat penalty | 1.0 |
+| Max new tokens | 131,072 |
+
+## Benchmarks (Original Model)
+
+### General Reasoning Tasks
+
+| Benchmark | Qwen3-4B | Qwen3-8B | Qwen3-14B | Qwen3-32B | Qwen3-30B-A3B | **Nanbeige4.1-3B** |
+|-----------|----------|----------|-----------|-----------|----------------|-------------------|
+| **Code** | | | | | | |
+| Live-Code-Bench-V6 | 57.4 | 49.4 | 55.9 | 55.7 | 66.0 | **76.9** |
+| LCB-Pro-Easy | 40.2 | 41.2 | 33.0 | 42.3 | 60.8 | **81.4** |
+| LCB-Pro-Medium | 5.3 | 3.5 | 1.8 | 3.5 | 3.5 | **28.1** |
+| **Math** | | | | | | |
+| AIME 2026 I | 81.46 | 70.42 | 76.46 | 75.83 | 87.30 | **87.40** |
+| HMMT Nov | 68.33 | 48.33 | 56.67 | 57.08 | 71.25 | **77.92** |
+| IMO-Answer-Bench | 48.00 | 36.56 | 41.81 | 43.94 | 54.34 | **53.38** |
+| **Science** | | | | | | |
+| GPQA | 65.8 | 62.0 | 63.38 | 68.4 | 73.4 | **83.8** |
+| HLE (Text-only) | 6.72 | 5.28 | 7.00 | 9.31 | 11.77 | **12.60** |
+| **Alignment** | | | | | | |
+| Arena-Hard-v2 | 34.9 | 26.3 | 36.9 | 56.0 | 60.2 | **73.2** |
+| Multi-Challenge | 41.14 | 36.30 | 36.97 | 38.72 | 49.40 | **52.21** |
+| **Tool Use** | | | | | | |
+| BFCL-V4 | 44.87 | 42.20 | 45.14 | 47.90 | 48.6 | **56.50** |
+| Tau2-Bench | 45.9 | 42.06 | 44.96 | 45.26 | 47.70 | **48.57** |
+
+### Deep Search Benchmarks
+
+| Model | xBench-DS-2505 | xBench-DS-2510 | Browse-Comp | GAIA | HLE | SEAL-0 |
+|-------|:-:|:-:|:-:|:-:|:-:|:-:|
+| MiroThinker-v1.0-8B | 61 | - | 31.1 | 66.4 | 21.5 | 40.4 |
+| AgentCPM-Explore-4B | 70 | - | 25.0 | 63.9 | 19.1 | 40.0 |
+| Qwen3-32B | 39 | 8 | 3.15 | 30.17 | 9.26 | 8.15 |
+| **Nanbeige4.1-3B** | **75** | **39** | **19.12** | **69.90** | **22.29** | **41.44** |
+
+## Files Included
+
+| File | Description |
+|------|-------------|
+| `model-00001-of-00002.safetensors` | Model weights (part 1) |
+| `model-00002-of-00002.safetensors` | Model weights (part 2) |
+| `model.safetensors.index.json` | Weight index mapping |
+| `config.json` | Model architecture configuration |
+| `tokenizer.json` | Fast tokenizer |
+| `tokenizer.model` | SentencePiece model |
+| `tokenizer_config.json` | Tokenizer config with all special tokens |
+| `tokenizer_config_search.json` | Tokenizer config for deep-search mode |
+| `chat_template.jinja` | Chat template (chat + tool calling + reasoning) |
+| `special_tokens_map.json` | Special tokens mapping |
+| `added_tokens.json` | Additional token definitions |
+| `generation_config.json` | Default generation parameters |
+
+## Special Tokens
+
+| Token | ID | Purpose |
+|-------|-----|---------|
+| `<\|im_start\|>` | 166100 | BOS / message start |
+| `<\|im_end\|>` | 166101 | EOS / message end |
+| `<\|endoftext\|>` | 166102 | End of text |
+| `<think>` | 166103 | Start of reasoning |
+| `</think>` | 166104 | End of reasoning |
+| `<tool_call>` | 166105 | Start of tool call |
+| `</tool_call>` | 166106 | End of tool call |
+
+## Deep-Search Mode
+
+For deep-search agent capabilities, switch to `tokenizer_config_search.json` and use the [miroflow-framework](https://github.com/MiroMindAI/MiroThinker) for inference. See the [original model card](https://huggingface.co/Nanbeige/Nanbeige4.1-3B) for detailed deep-search setup instructions.
+
+## Important: Extended Thinking Behavior
+
+Nanbeige4.1-3B is a **reasoning model** that generates chain-of-thought inside `<think>...</think>` tags before producing a final answer. This is by design — and it affects **all variants equally** (4-bit, 8-bit, BF16).
+
+### What to expect
+
+| Task Type | Typical Thinking Length | Recommended `max_tokens` |
+|-----------|:----------------------:|:------------------------:|
+| Math, logic, tool calling | 200–500 tokens | 512–2,048 |
+| Code generation | 500–1,500 tokens | 2,048–4,096 |
+| Translation, creative writing, commonsense | 3,000–5,000+ tokens | **8,192+** |
+
+For complex tasks (translation, creative writing, open-ended questions), the model may spend **3,000–5,000+ tokens** reasoning before delivering an answer. If `max_tokens` is too low, the output will be truncated mid-thinking and **no final answer will appear** — this is not a failure, the model simply needs more tokens to finish its thought process.
+
+### Workarounds
+
+**1. Increase `max_tokens` (recommended)**
+
+```python
+# For complex tasks, use high max_tokens
+response = generate(
+    model, tokenizer, prompt=prompt,
+    max_tokens=8192,  # or higher for very complex tasks
+    sampler=sampler
+)
+```
+
+**2. Skip thinking (experimental)**
+
+You can pre-fill an empty thinking block to force the model to answer directly. This does not always work — the model may re-enter thinking mode — but it can help for simpler open-ended tasks:
+
+```python
+prompt = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+prompt += '<think>\n\n</think>\n\n'  # Force empty thinking block
+response = generate(
+    model, tokenizer, prompt=prompt,
+    max_tokens=512, sampler=sampler
+)
+```
+
+**3. Post-process to extract the answer**
+
+```python
+if '</think>' in response:
+    answer = response.split('</think>')[-1].strip()
+else:
+    answer = response  # Still in thinking — increase max_tokens
+```
+
+## Limitations
+
+While safety was emphasized during training, the model may still generate unexpected outputs due to its size and probabilistic nature. Users should not propagate harmful content. The developers assume no responsibility for consequences from dissemination of inappropriate content.
+
+## License
+
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), the same license as the [original Nanbeige4.1-3B model](https://huggingface.co/Nanbeige/Nanbeige4.1-3B).
+
+## Credits
+
+- **Original model:** [Nanbeige/Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B) by [Nanbeige (BOSS Zhipin)](https://huggingface.co/Nanbeige)
+- **MLX framework:** [Apple MLX](https://github.com/ml-explore/mlx)
+- **Conversion:** [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms)
+- **Contact (original model):** [nanbeige@kanzhun.com](mailto:nanbeige@kanzhun.com)

added_tokens.json

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+{
+  "</think>": 166104,
+  "</tool_call>": 166106,
+  "<think>": 166103,
+  "<tool_call>": 166105,
+  "<|endoftext|>": 166102,
+  "<|im_end|>": 166101,
+  "<|im_start|>": 166100
+}

chat_template.jinja

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+
+        {%- if tools %}
+            {{- '<|im_start|>system
+' }}
+            {%- if messages[0].role == 'system' %}
+                {{- messages[0].content + '
+
+' }}
+            {%- else %} 
+                {{- '你是一位工具函数调用专家，你会得到一个问题和一组可能的工具函数。根据问题，你需要进行一个或多个函数/工具调用以实现目的，请尽量尝试探索通过工具解决问题。
+如果没有一个函数可以使用，请直接使用自然语言回复用户。
+如果给定的问题缺少函数所需的参数，请使用自然语言进行提问，向用户询问必要信息。
+如果调用结果已经足够回答用户问题，请对历史结果进行总结，使用自然语言回复用户。' }} 
+            {%- endif %}
+            {{- "# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>" }}
+            {%- for tool in tools %}
+                {{- "
+" }}
+                {{- tool | tojson }}
+            {%- endfor %}
+            {{- "
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{\"name\": <function-name>, \"arguments\": <args-json-object>}
+</tool_call><|im_end|>
+" }}
+        {%- else %}
+            {%- if messages[0].role == 'system' %}
+                {{- '<|im_start|>system
+' + messages[0].content + '<|im_end|>
+' }}
+            {%- else %} 
+                {{- '<|im_start|>system
+你是南北阁，一款由BOSS直聘自主研发并训练的专业大语言模型。<|im_end|>
+' }} 
+            {%- 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 + '
+' + content + '<|im_end|>' + '
+' }}
+            {%- 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('
+').split('<think>')[-1].lstrip('
+') %}
+                        {%- set content = content.split('</think>')[-1].lstrip('
+') %}
+                    {%- endif %}
+                {%- endif %}
+                {%- if loop.index0 > ns.last_query_index or keep_all_think or (extra_body is defined and extra_body.keep_all_think) %}
+                    {%- if loop.last or (not loop.last and reasoning_content) %}
+                        {{- '<|im_start|>' + message.role + '
+<think>
+' + reasoning_content.strip('
+') + '
+</think>
+
+' + content.lstrip('
+') }}
+                    {%- else %}
+                        {{- '<|im_start|>' + message.role + '
+' + content }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '<|im_start|>' + message.role + '
+' + content }}
+                {%- endif %}
+                {%- if message.tool_calls %}
+                    {%- for tool_call in message.tool_calls %}
+                        {%- if (loop.first and content) or (not loop.first) %}
+                            {{- '
+' }}
+                        {%- endif %}
+                        {%- if tool_call.function %}
+                            {%- set tool_call = tool_call.function %}
+                        {%- endif %}
+                        {{- '<tool_call>
+{"name": "' }}
+                        {{- tool_call.name }}
+                        {{- '", "arguments": ' }}
+                        {%- if tool_call.arguments is string %}
+                            {{- tool_call.arguments }}
+                        {%- else %}
+                            {{- tool_call.arguments | tojson }}
+                        {%- endif %}
+                        {{- '}
+</tool_call>' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '<|im_end|>
+' }}
+            {%- elif message.role == "tool" %}
+                {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+                    {{- '<|im_start|>user' }}
+                {%- endif %}
+                {{- '
+<tool_response>
+' }}
+                {{- content }}
+                {{- '
+</tool_response>' }}
+                {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+                    {{- '<|im_end|>
+' }}
+                {%- endif %}
+            {%- endif %}
+        {%- endfor %}
+        {%- if add_generation_prompt %}
+            {{- '<|im_start|>assistant
+' }}
+        {%- endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 166100,
+    "dtype": "bfloat16",
+    "embd_pdrop": 0.0,
+    "eos_token_id": 166101,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 10496,
+    "max_position_embeddings": 262144,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 20,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 4,
+    "pad_token_id": 0,
+    "pretraining_tp": 1,
+    "resid_pdrop": 0.0,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 70000000,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.51.3",
+    "use_cache": true,
+    "vocab_size": 166144
+}

generation_config.json

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