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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,278 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- int8
+- quantized
+- llm-compressor
+- compressed-tensors
+- red hat
+base_model:
+- Qwen/Qwen3-Next-80B-A3B-Instruct
+---
+
+
+# Qwen3-Next-80B-A3B-Instruct-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen3NextForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 
+- **Version:** 1.0
+- **Model Developers:**: Red Hat
+
+Quantized version of [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) to INT8 data type.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
+Only the weights and activations of the linear operators within transformers blocks of the language model are quantized. 
+
+## Deployment
+
+### Use with vLLM
+
+1. Initialize vLLM server:
+```bash
+vllm serve RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8 --tensor_parallel_size 2
+```
+
+2. Send requests to the server:
+
+```python
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://<your-server-host>:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+model = "RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8"
+
+messages = [
+    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
+]
+
+
+outputs = client.chat.completions.create(
+    model=model,
+    messages=messages,
+)
+
+generated_text = outputs.choices[0].message.content
+print(generated_text)
+```
+
+## Creation
+
+This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below.
+
+<details>
+  <summary>Creation details</summary>
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.utils import dispatch_for_generation
+
+# NOTE: Requires a minimum of transformers 4.57.0
+
+MODEL_ID = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(
+    targets="Linear", scheme="W8A8", ignore=[
+        "lm_head",
+        "re:.*mlp.gate$",
+        "re:.*mlp.shared_expert_gate$",
+        "re:.*linear_attn.*",
+    ],
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-quantized.w8a8"
+model.save_pretrained(SAVE_DIR)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+</details>
+
+
+## Evaluation
+
+The model was evaluated on the AIME25, GPQA Diamond and Mathh 500 benchmarks using [lighteval](https://github.com/huggingface/lighteval), and on MMLU-Pro, IFEval and GSM8k using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
+In all cases [vLLM](https://vllm.ai) is used as the backend.
+All results were averaged over 6 repetitions with different random seeds.
+
+
+<details>
+  <summary>Evaluation commands</summary>
+
+### Start vLLM server
+```bash
+vllm serve RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8 --tensor_parallel_size 2
+```
+
+### lm-evaluation-harness
+```bash
+lm_eval --model local-chat-completions \
+  --tasks mmlu_pro_chat \
+  --model_args "model=RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,timeout=600,tokenizer_backend=None" \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --fewshot_as_multiturn \
+  --output_path mmlu_pro_qwen3_next_w8a8 \
+  --gen_kwargs "do_sample=True,temperature=0.7,top_p=0.8,top_k=20,max_gen_toks=16000"
+```
+
+```bash
+lm_eval --model local-chat-completions \
+  --tasks ifeval \
+  --model_args "model=RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,timeout=600,tokenizer_backend=None" \
+  --apply_chat_template \
+  --output_path ifeval_qwen3_next_w8a8 \
+  --gen_kwargs "do_sample=True,temperature=0.7,top_p=0.8,top_k=20,max_gen_toks=16000"
+```
+
+```bash
+lm_eval --model local-chat-completions \
+  --tasks gsm8k \
+  --model_args "model=RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,timeout=600,tokenizer_backend=None" \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --fewshot_as_multiturn \
+  --output_path gsm8k_qwen3_next_w8a8 \
+  --gen_kwargs "do_sample=True,temperature=0.7,top_p=0.8,top_k=20,max_gen_toks=16000"
+```
+
+### lighteval
+litellm_config.yaml
+```yaml
+model_parameters:
+  provider: "hosted_vllm"
+  model_name: "hosted_vllm/RedHatAI/Phi-4-reasoning-FP8-dynamic"
+  base_url: "http://0.0.0.0:8000/v1"
+  api_key: ""
+  timeout: 600
+  concurrent_requests: 128
+  generation_parameters:
+    temperature: 0.7
+    top_k: 20
+    top_p: 0.8
+    max_new_tokens: 16000
+```
+
+```bash
+lighteval endpoint litellm litellm_config.yaml \
+    gpqa:diamond|0,math_500|0,aime25|0 \
+    --output-dir qwen3_next_w8a8 \
+    --save-details
+```
+</details>
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Qwen3-Next-80B-A3B-Instruct</strong>
+   </td>
+   <td><strong>Qwen3-Next-80B-A3B-Instruct-quantized.w8a8<br>(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>AIME25
+   </td>
+   <td>62.78
+   </td>
+   <td>65.00
+   </td>
+   <td>103.5%
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA Diamond
+   </td>
+   <td>74.58
+   </td>
+   <td>75.17
+   </td>
+   <td>100.8%
+   </td>
+  </tr>
+  <tr>
+   <td>Math 500
+   </td>
+   <td>89.73
+   </td>
+   <td>90.57
+   </td>
+   <td>100.9%
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-Pro
+   </td>
+   <td>78.62
+   </td>
+   <td>78.85
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval
+   </td>
+   <td>91.45
+   </td>
+   <td>91.51
+   </td>
+   <td>100.1%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM8k
+   </td>
+   <td>69.71
+   </td>
+   <td>69.74
+   </td>
+   <td>100.0%
+   </td>
+  </tr>
+</table>

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|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

@@ -0,0 +1,61 @@
+{%- 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 %}
+{%- 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" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- 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' }}
+{%- endif %}

config.json

@@ -0,0 +1,348 @@
+{
+  "architectures": [
+    "Qwen3NextForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "full_attention_interval": 4,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_types": [
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention"
+  ],
+  "linear_conv_kernel_dim": 4,
+  "linear_key_head_dim": 128,
+  "linear_num_key_heads": 16,
+  "linear_num_value_heads": 32,
+  "linear_value_head_dim": 128,
+  "max_position_embeddings": 262144,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_next",
+  "moe_intermediate_size": 512,
+  "norm_topk_prob": true,
+  "num_attention_heads": 16,
+  "num_experts": 512,
+  "num_experts_per_tok": 10,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 2,
+  "output_router_logits": false,
+  "partial_rotary_factor": 0.25,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "int-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "token",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "mse",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "channel",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "int-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.layers.0.linear_attn.in_proj_qkvz",
+      "model.layers.0.linear_attn.in_proj_ba",
+      "model.layers.0.linear_attn.out_proj",
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+      "model.layers.0.mlp.shared_expert_gate",
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