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Model: JongYeop/Qwen2.5-14B-Instruct-FP4-W4A4
Source: Original Platform

.gitattributes

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- quantization
+- fp4
+- nvfp4
+- compressed-tensors
+- qwen2
+- text-generation
+- 4bit
+base_model: Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+model-index:
+- name: Qwen2.5-14B-Instruct-FP4-W4A4
+  results: []
+quantization:
+  quant_method: compressed-tensors
+  bits: 4
+  type: float
+  format: nvfp4-pack-quantized
+  strategy: tensor_group
+  group_size: 16
+  symmetric: true
+---
+
+# Qwen2.5-14B-Instruct-FP4-W4A4
+
+## Model Description
+
+This is an NVFP4 (NVIDIA FP4) quantized version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) using the compressed-tensors quantization method.
+
+- **Base Model**: Qwen/Qwen2.5-14B-Instruct
+- **Quantization Method**: compressed-tensors
+- **Quantization Type**: NVFP4 W4A4 (4-bit Weight and Activation)
+- **Model Size**: ~10.5GB (compared to ~28GB for BF16)
+- **Compression Ratio**: ~2.7x
+
+## Quantization Configuration
+
+This model uses **NVFP4 (NVIDIA FP4) quantization** with grouped quantization for both weights and activations:
+
+### Weights
+- **Precision**: NVFP4 (4-bit floating point)
+- **Strategy**: Tensor-group (grouped quantization)
+- **Group Size**: 16
+- **Symmetric**: Yes
+- **Dynamic**: No (static quantization)
+- **Observer**: MinMax
+
+### Activations
+- **Precision**: NVFP4 (4-bit floating point)
+- **Strategy**: Tensor-group (grouped quantization)
+- **Group Size**: 16
+- **Symmetric**: Yes
+- **Dynamic**: Local (dynamic quantization with local calibration)
+- **Observer**: MinMax
+
+### Other Details
+- **Format**: nvfp4-pack-quantized (packed 4-bit format)
+- **KV Cache**: Not quantized
+- **Ignored Layers**: lm_head
+- **Target Layers**: Linear layers
+- **Quantization Version**: 0.11.0
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "JongYeop/Qwen2.5-14B-Instruct-FP4-W4A4"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Load quantized model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto"
+)
+
+# Generate text
+messages = [
+    {"role": "user", "content": "What is machine learning?"}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Model Architecture
+
+- **Architecture**: Qwen2ForCausalLM
+- **Hidden Size**: 5120
+- **Intermediate Size**: 13824
+- **Number of Layers**: 48
+- **Number of Attention Heads**: 40
+- **Number of KV Heads**: 8
+- **Vocabulary Size**: 152064
+- **Max Position Embeddings**: 32768
+
+## Intended Use
+
+This quantized model is intended for efficient inference with significantly reduced memory footprint while maintaining reasonable performance. It is suitable for:
+
+- Resource-constrained environments
+- Edge deployment
+- Applications requiring minimal memory usage
+- High throughput scenarios
+- GPU inference with FP4 support
+
+## Limitations
+
+- FP4 quantization may result in more accuracy loss compared to FP8 or INT8 quantization
+- Best performance is achieved on hardware with native FP4 support (e.g., NVIDIA H100, Ada Lovelace, Blackwell GPUs)
+- Dynamic activation quantization may introduce additional runtime overhead
+- Grouped quantization requires compatible inference engines
+
+## Performance Notes
+
+- **Memory Usage**: ~2.7x reduction compared to BF16
+- **Speed**: Requires hardware with FP4 tensor core support for optimal performance
+- **Accuracy**: May experience some degradation compared to higher precision formats
+
+## Citation
+
+If you use this model, please cite the original Qwen2.5 paper and the compressed-tensors library.
+
+## License
+
+Same as the base model: [Apache 2.0](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)

added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "nvfp4-pack-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": "local",
+          "group_size": 16,
+          "num_bits": 4,
+          "observer": "minmax",
+          "observer_kwargs": {},
+          "strategy": "tensor_group",
+          "symmetric": true,
+          "type": "float"
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 16,
+          "num_bits": 4,
+          "observer": "minmax",
+          "observer_kwargs": {},
+          "strategy": "tensor_group",
+          "symmetric": true,
+          "type": "float"
+        }
+      }
+    },
+    "format": "nvfp4-pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "lm_head"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.11.0"
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.55.2"
+}

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recipe.yaml

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+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      targets: [Linear]
+      ignore: [lm_head]
+      scheme: NVFP4

special_tokens_map.json

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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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+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 11421896

tokenizer_config.json

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+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+}