Repository: huggingface/transformers
Pull request: Fix format of compressed_tensors.md

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Changed files:
filename: docs/source/en/quantization/compressed_tensors.md
patch: @@ -65,11 +65,11 @@ print(f"{mem_params/2**30:.4f} GB")
 
 ## Model checkpoint
 
-compressed-tensor models are defined through its configuration entry. The following example is taken from the [nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf](https://huggingface.co/nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf/blob/main/config.json) `config.json` file.
+Compressed-tensor models are defined through its configuration entry. The following example is taken from the [nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf](https://huggingface.co/nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf/blob/main/config.json) `config.json` file.
 
 There are a lot of entries to allow for flexible expression both during and after compression, but the entries for loading and inference can be simplified to focus on just a few key entries.
 
-```yaml
+```json
 "quantization_config": {
   "config_groups": {
     "group_0": {
@@ -97,31 +97,31 @@ The config file specifies the quantization of a config group (`group_0`), which
 
 For a more detailed look at the model weights, use the [safetensors viewer](https://huggingface.co/nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf?show_file_info=model.safetensors.index.json) on the model card to see the quantized weights, input scale, and weight scale for all [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) modules.
 
-| Tensors | Shape |	Precision |
+| Tensors | Shape | Precision |
 | ------- | ----- | --------- |
-model.layers.0.input_layernorm.weight	| [4 096]	| BF16
-model.layers.0.mlp.down_proj.input_scale	| [1]	| BF16
-model.layers.0.mlp.down_proj.weight	| [4 096, 14 336] |	F8_E4M3
-model.layers.0.mlp.down_proj.weight_scale |	[1]	| BF16
-model.layers.0.mlp.gate_proj.input_scale |	[1]	| BF16
-model.layers.0.mlp.gate_proj.weight	| [14 336, 4 096]	| F8_E4M3
-model.layers.0.mlp.gate_proj.weight_scale	| [1] |	BF16
-model.layers.0.mlp.up_proj.input_scale|	[1]	|BF16
-model.layers.0.mlp.up_proj.weight |	[14 336, 4 096]	| F8_E4M3
-model.layers.0.mlp.up_proj.weight_scale | [1]	| BF16
-model.layers.0.post_attention_layernorm.weight |	[4 096]	|BF16
-model.layers.0.self_attn.k_proj.input_scale |	[1]	|  BF16
-model.layers.0.self_attn.k_proj.weight |	[1 024, 4 096]|	F8_E4M3
-model.layers.0.self_attn.k_proj.weight_scale |[1]	| BF16
-model.layers.0.self_attn.o_proj.input_scale	| [1]	| BF16
-model.layers.0.self_attn.o_proj.weight | [4 096, 4 096]	| F8_E4M3
-model.layers.0.self_attn.o_proj.weight_scale | [1]	| BF16
-model.layers.0.self_attn.q_proj.input_scale	| [1]	| BF16
-model.layers.0.self_attn.q_proj.weight | [4 096, 4 096]	| F8_E4M3
-model.layers.0.self_attn.q_proj.weight_scale |	[1] | BF16
-model.layers.0.self_attn.v_proj.input_scale	| [1] | BF16
-model.layers.0.self_attn.v_proj.weight |	[1 024, 4 096]	| F8_E4M3
-model.layers.0.self_attn.v_proj.weight_scale |	[1] |	BF16
+|model.layers.0.input_layernorm.weight | [4 096] | BF16|
+|model.layers.0.mlp.down_proj.input_scale | [1] | BF16|
+|model.layers.0.mlp.down_proj.weight | [4 096, 14 336] | F8_E4M3|
+|model.layers.0.mlp.down_proj.weight_scale | [1] | BF16|
+|model.layers.0.mlp.gate_proj.input_scale | [1] | BF16|
+|model.layers.0.mlp.gate_proj.weight | [14 336, 4 096] | F8_E4M3|
+|model.layers.0.mlp.gate_proj.weight_scale | [1] | BF16|
+|model.layers.0.mlp.up_proj.input_scale| [1] |BF16|
+|model.layers.0.mlp.up_proj.weight | [14 336, 4 096] | F8_E4M3|
+|model.layers.0.mlp.up_proj.weight_scale | [1] | BF16|
+|model.layers.0.post_attention_layernorm.weight | [4 096] |BF16|
+|model.layers.0.self_attn.k_proj.input_scale | [1] |  BF16|
+|model.layers.0.self_attn.k_proj.weight | [1 024, 4 096]| F8_E4M3|
+|model.layers.0.self_attn.k_proj.weight_scale |[1] | BF16|
+|model.layers.0.self_attn.o_proj.input_scale | [1] | BF16|
+|model.layers.0.self_attn.o_proj.weight | [4 096, 4 096] | F8_E4M3|
+|model.layers.0.self_attn.o_proj.weight_scale | [1] | BF16|
+|model.layers.0.self_attn.q_proj.input_scale | [1] | BF16|
+|model.layers.0.self_attn.q_proj.weight | [4 096, 4 096] | F8_E4M3|
+|model.layers.0.self_attn.q_proj.weight_scale | [1] | BF16|
+|model.layers.0.self_attn.v_proj.input_scale | [1] | BF16|
+|model.layers.0.self_attn.v_proj.weight | [1 024, 4 096] | F8_E4M3|
+|model.layers.0.self_attn.v_proj.weight_scale | [1] | BF16|
 
 When loading a compressed-tensors model with the [`~quantizers.HFQuantizer`] integration, all the [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) modules specified in the quantization config are replaced by [CompressedLinear](https://github.com/neuralmagic/compressed-tensors/blob/975cb223b19fcac2b98a4271d17668462d4d6e1d/src/compressed_tensors/linear/compressed_linear.py#L30) modules that manage the compressed weights and forward pass for inference. The `lm_head` module is still kept as an unquantized nn.Linear module.