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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
+- mistral
+- text-generation
+- 4bit
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+pipeline_tag: text-generation
+model-index:
+- name: Mistral-7B-Instruct-v0.2-FP4-W4A4
+  results: []
+quantization:
+  quant_method: compressed-tensors
+  bits: 4
+  type: float
+  format: nvfp4-pack-quantized
+  strategy: tensor_group
+  group_size: 16
+  symmetric: true
+---
+
+# Mistral-7B-Instruct-v0.2-FP4-W4A4
+
+## Model Description
+
+This is an NVFP4 (NVIDIA FP4) quantized version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using the compressed-tensors quantization method.
+
+- **Base Model**: mistralai/Mistral-7B-Instruct-v0.2
+- **Quantization Method**: compressed-tensors
+- **Quantization Type**: NVFP4 W4A4 (4-bit Weight and Activation)
+- **Model Size**: ~4.2GB (compared to ~14GB for BF16)
+- **Compression Ratio**: ~3.3x
+
+## 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/Mistral-7B-Instruct-v0.2-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**: MistralForCausalLM
+- **Hidden Size**: 4096
+- **Intermediate Size**: 14336
+- **Number of Layers**: 32
+- **Number of Attention Heads**: 32
+- **Number of KV Heads**: 8
+- **Vocabulary Size**: 32000
+- **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**: ~3.3x 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 Mistral paper and the compressed-tensors library.
+
+## License
+
+Same as the base model: [Apache 2.0](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)

chat_template.jinja

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+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content'] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{{- bos_token }}
+{%- for message in loop_messages %}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
+        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}
+    {%- endif %}
+    {%- if message['role'] == 'user' %}
+        {%- if loop.first and system_message is defined %}
+            {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}
+        {%- else %}
+            {{- ' [INST] ' + message['content'] + ' [/INST]' }}
+        {%- endif %}
+    {%- elif message['role'] == 'assistant' %}
+        {{- ' ' + message['content'] + eos_token}}
+    {%- else %}
+        {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}
+    {%- endif %}
+{%- endfor %}

config.json

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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": null,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "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-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.55.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:698bb20f2aec96050ef7a9d780ee64cf767dfc532034ecf69d3f12bee534b983
+size 4450800712

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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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}