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Model: RedHatAI/Mistral-7B-Instruct-v0.3-quantized.w8a16
Source: Original Platform

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README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+license: apache-2.0
+license_link: https://www.apache.org/licenses/LICENSE-2.0
+---
+
+# Mistral-7B-Instruct-v0.3-quantized.w8a16
+
+## Model Overview
+- **Model Architecture:** Mistral
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3), 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). Use in languages other than English.
+- **Release Date:** 7/10/2024
+- **Version:** 1.0
+- **License(s):** [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Model Developers:** Neural Magic
+
+Quantized version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
+It achieves an average score of 66.43 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 66.74.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) 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 of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
+[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 1% damping factor and 256 sequences of 8,192 random tokens.
+
+
+## Deployment
+
+### Use with vLLM
+
+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 = "neuralmagic/Mistral-7B-Instruct-v0.3-quantized.w8a16"
+
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompts = tokenizer.apply_chat_template(messages, tokenize=False)
+
+llm = LLM(model=model_id)
+
+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.
+
+### Use with transformers
+
+This model is supported by Transformers leveraging the integration with the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) data format.
+The following example contemplates how the model can be used using the `generate()` function.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "neuralmagic/Mistral-7B-Instruct-v0.3-quantized.w8a16"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
+
+## Creation
+
+This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below.
+Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ.
+
+```python
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import random
+
+model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+
+num_samples = 256
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+max_token_id = len(tokenizer.get_vocab()) - 1
+examples = []
+for _ in range(num_samples):
+  examples.append(
+  {
+    "input_ids": [random.randint(0, max_token_id) for _ in range(max_seq_len)],
+    "attention_mask": max_seq_len*[1],
+  }
+)
+
+quantize_config = BaseQuantizeConfig(
+  bits=8,
+  group_size=-1,
+  desc_act=False,
+  model_file_base_name="model",
+  damp_percent=0.01,
+)
+
+model = AutoGPTQForCausalLM.from_pretrained(
+  model_id,
+  quantize_config,
+  device_map="auto",
+)
+
+model.quantize(examples)
+model.save_pretrained("Mistral-7B-Instruct-v0.3-quantized.w8a16")
+```
+
+
+
+## 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/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Mistral-7B-Instruct-v0.3-quantized.w8a16",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Mistral-7B-Instruct-v0.3 </strong>
+   </td>
+   <td><strong>Mistral-7B-Instruct-v0.3-quantized.w8a16(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>61.84
+   </td>
+   <td>62.06
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>63.74
+   </td>
+   <td>64.26
+   </td>
+   <td>100.8%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>49.28
+   </td>
+   <td>50.42
+   </td>
+   <td>102.3%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>84.84
+   </td>
+   <td>84.78
+   </td>
+   <td>99.9%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>79.56
+   </td>
+   <td>79.48
+   </td>
+   <td>99.9%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>59.33
+   </td>
+   <td>59.48
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>66.43</strong>
+   </td>
+   <td><strong>66.74</strong>
+   </td>
+   <td><strong>100.5%</strong>
+   </td>
+  </tr>
+</table>

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configuration.json

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generation_config.json

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+    "single_word": false
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