Upload Qwen2.5-7B-FP8-SlideSparse-2_10

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Qwen2.5-7B-FP8-SlideSparse-2_10/README.md

@@ -0,0 +1,420 @@
+---
+language:
+- zh
+- en
+- fr
+- es
+- pt
+- de
+- it
+- ru
+- ja
+- ko
+- vi
+- th
+- ar
+- id
+- tr
+- fa
+- nl
+- pl
+- cs
+- he
+- sv
+- fi
+- da
+- no
+- el
+- bg
+- uk
+- ur
+- sr
+- ms
+- zsm
+- nld
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwen2_5
+- qwen2_5_instruct
+- fp8
+- quantized
+- conversational
+- text-generation-inference
+- compressed-tensors
+license: apache-2.0
+license_name: apache-2.0
+name: RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic
+description: This model was obtained by quantizing activations and weights of Qwen2.5-7B-Instruct to FP8 data type.
+readme: https://huggingface.co/RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic/main/README.md
+tasks:
+- text-to-text
+provider: Alibaba Cloud
+license_link: https://www.apache.org/licenses/LICENSE-2.0
+validated_on:
+  - RHOAI 2.20
+  - RHAIIS 3.0
+  - RHELAI 1.5
+---
+
+<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
+  Qwen2.5-7B-Instruct-FP8-dynamic
+  <img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
+</h1>
+  
+<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
+<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
+</a>
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** FP8
+  - **Weight quantization:** FP8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 11/27/2024
+- **Version:** 1.0
+- **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
+- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+### Model Optimizations
+
+This model was obtained by quantizing activations and weights of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to FP8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
+The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
+
+## Deployment
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "user", "content": "Give me a short introduction to large language model."},
+]
+
+prompts = tokenizer.apply_chat_template(messages, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompts, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+<details>
+  <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
+  
+```bash
+podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
+ --ipc=host \
+--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
+--name=vllm \
+registry.access.redhat.com/rhaiis/rh-vllm-cuda \
+vllm serve \
+--tensor-parallel-size 8 \
+--max-model-len 32768  \
+--enforce-eager --model RedHatAI/Qwen2.5-7B-Instruct-FP8-dynamic
+```
+​​See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
+</details>
+
+<details>
+  <summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
+  
+```bash
+# Download model from Red Hat Registry via docker
+# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
+ilab model download --repository docker://registry.redhat.io/rhelai1/qwen2-5-7b-instruct-fp8-dynamic:1.5
+```
+
+```bash
+# Serve model via ilab
+ilab model serve --model-path ~/.cache/instructlab/models/qwen2-5-7b-instruct-fp8-dynamic
+  
+# Chat with model
+ilab model chat --model ~/.cache/instructlab/models/qwen2-5-7b-instruct-fp8-dynamic
+```
+See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
+</details>
+
+<details>
+  <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
+  
+```python
+# Setting up vllm server with ServingRuntime
+# Save as: vllm-servingruntime.yaml
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+ name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
+ annotations:
+   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
+   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
+ labels:
+   opendatahub.io/dashboard: 'true'
+spec:
+ annotations:
+   prometheus.io/port: '8080'
+   prometheus.io/path: '/metrics'
+ multiModel: false
+ supportedModelFormats:
+   - autoSelect: true
+     name: vLLM
+ containers:
+   - name: kserve-container
+     image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm
+     command:
+       - python
+       - -m
+       - vllm.entrypoints.openai.api_server
+     args:
+       - "--port=8080"
+       - "--model=/mnt/models"
+       - "--served-model-name={{.Name}}"
+     env:
+       - name: HF_HOME
+         value: /tmp/hf_home
+     ports:
+       - containerPort: 8080
+         protocol: TCP
+```
+
+```python
+# Attach model to vllm server. This is an NVIDIA template
+# Save as: inferenceservice.yaml
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  annotations:
+    openshift.io/display-name: Qwen2.5-7B-Instruct-FP8-dynamic # OPTIONAL CHANGE
+    serving.kserve.io/deploymentMode: RawDeployment
+  name: Qwen2.5-7B-Instruct-FP8-dynamic         # specify model name. This value will be used to invoke the model in the payload
+  labels:
+    opendatahub.io/dashboard: 'true'
+spec:
+  predictor:
+    maxReplicas: 1
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: vLLM
+      name: ''
+      resources:
+        limits:
+          cpu: '2'			# this is model specific
+          memory: 8Gi		# this is model specific
+          nvidia.com/gpu: '1'	# this is accelerator specific
+        requests:			# same comment for this block
+          cpu: '1'
+          memory: 4Gi
+          nvidia.com/gpu: '1'
+      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
+      storageUri: oci://registry.redhat.io/rhelai1/modelcar-qwen2-5-7b-instruct-fp8-dynamic:1.5
+    tolerations:
+    - effect: NoSchedule
+      key: nvidia.com/gpu
+      operator: Exists
+```
+
+```bash
+# make sure first to be in the project where you want to deploy the model
+# oc project <project-name>
+# apply both resources to run model
+# Apply the ServingRuntime
+oc apply -f vllm-servingruntime.yaml
+# Apply the InferenceService
+oc apply -f qwen-inferenceservice.yaml
+```
+
+```python
+# Replace <inference-service-name> and <cluster-ingress-domain> below:
+# - Run `oc get inferenceservice` to find your URL if unsure.
+# Call the server using curl:
+curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
+        -H "Content-Type: application/json" \
+        -d '{
+    "model": "Qwen2.5-7B-Instruct-FP8-dynamic",
+    "stream": true,
+    "stream_options": {
+        "include_usage": true
+    },
+    "max_tokens": 1,
+    "messages": [
+        {
+            "role": "user",
+            "content": "How can a bee fly when its wings are so small?"
+        }
+    ]
+}'
+```
+
+See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
+</details>
+
+## Creation
+
+<details>
+  <summary>Creation details</summary>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+  ```python
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from llmcompressor.modifiers.quantization import QuantizationModifier
+  from llmcompressor.transformers import oneshot
+  
+  # Load model
+  model_stub = "Qwen/Qwen2.5-7B-Instruct-FP8-dynamic"
+  model_name = model_stub.split("/")[-1]
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+  
+  model = AutoModelForCausalLM.from_pretrained(
+      model_stub,
+      device_map="auto",
+      torch_dtype="auto",
+  )
+  
+  # Configure the quantization algorithm and scheme
+  recipe = QuantizationModifier(
+      targets="Linear",
+      scheme="FP8_dynamic",
+      ignore=["lm_head"],
+  )
+  
+  # Apply quantization
+  oneshot(
+      model=model,
+      recipe=recipe,
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-FP8-dynamic"
+  model.save_pretrained(save_path)
+  tokenizer.save_pretrained(save_path)
+  print(f"Model and tokenizer saved to: {save_path}")
+  ```
+</details>
+
+## Evaluation
+
+The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2.5-7B-Instruct-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=4096,enable_chunk_prefill=True,add_bos_token=True,tensor_parallel_size=1 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <th>Benchmark
+   </th>
+   <th>Qwen2.5-7B-Instruct
+   </th>
+   <th>Qwen2.5-7B-Instruct-FP8-dynamic<br>(this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>74.24
+   </td>
+   <td>74.04
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>63.40
+   </td>
+   <td>63.14
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>80.36
+   </td>
+   <td>80.06
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>81.52
+   </td>
+   <td>81.11
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>74.66
+   </td>
+   <td>74.43
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>64.76
+   </td>
+   <td>64.87
+   </td>
+   <td>100.2%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>73.16</strong>
+   </td>
+   <td><strong>72.94</strong>
+   </td>
+   <td><strong>99.7%</strong>
+   </td>
+  </tr>
+</table>
+

Qwen2.5-7B-FP8-SlideSparse-2_10/added_tokens.json

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Qwen2.5-7B-FP8-SlideSparse-2_10/config.json

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+  "model_type": "qwen2",
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+    "quantization_status": "compressed"
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+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

Qwen2.5-7B-FP8-SlideSparse-2_10/generation_config.json

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Qwen2.5-7B-FP8-SlideSparse-2_10/recipe.yaml

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+quant_stage:
+  quant_modifiers:
+    QuantizationModifier:
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          targets: [Linear]
+          weights: {num_bits: 8, type: float, symmetric: true, strategy: channel, observer: mse}
+          input_activations: {num_bits: 8, type: float, symmetric: true, strategy: token,
+            dynamic: true, observer: memoryless}

Qwen2.5-7B-FP8-SlideSparse-2_10/slidesparse_config.json

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+{
+  "Z": 2,
+  "L": 10,
+  "N": 5,
+  "window_size": 4,
+  "stride": 2,
+  "num_windows": 4,
+  "expand_ratio": 1.6,
+  "in_group_size": 10,
+  "out_group_size": 16,
+  "extra": {
+    "mode": "magnitude",
+    "skip_prune": false,
+    "skip_slide": false,
+    "skip_compress": true
+  }
+}

Qwen2.5-7B-FP8-SlideSparse-2_10/special_tokens_map.json

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+{
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+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
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+    "content": "<|im_end|>",
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+    "normalized": false,
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+    "single_word": false
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

Qwen2.5-7B-FP8-SlideSparse-2_10/tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:913950e4971737031da511cdd1b410daae4566f62eb845b3975bca5a102323d8
+size 11421995

Qwen2.5-7B-FP8-SlideSparse-2_10/tokenizer_config.json

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+}