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Model: amazon/MistralLite-AWQ
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+inference: false
+---
+
+# MistralLite-AWQ Model
+
+MistralLite-AWQ is a version of the [MistralLite](https://huggingface.co/amazon/MistralLite) model that was
+quantized using the AWQ method developed by [Lin et al. (2023)](https://arxiv.org/abs/2306.00978).
+The MistralLite-AWQ models are approximately **70% smaller** than those of MistralLite whilst maintaining comparable performance.
+
+Please refer to the [original MistralLite model card](https://huggingface.co/amazon/MistralLite) for details about the model
+preparation and training processes.
+
+## MistralLite-AWQ Variants
+
+| Branch | Approx. Model Size | `q_group_size` | `w_bit` | `version` |
+|--------|---:|---------------:|--------:|-----------|
+| [main](https://huggingface.co/amazon/MistralLite-AWQ/tree/main) | 3.9 GB | 128 | 4 | GEMM |
+| [MistralLite-AWQ-64g-4b-GEMM](https://huggingface.co/amazon/MistralLite-AWQ/tree/MistralLite-AWQ-64g-4b-GEMM) | 4.0 GB | 64 | 4 | GEMM |
+| [MistralLite-AWQ-32g-4b-GEMM](https://huggingface.co/amazon/MistralLite-AWQ/tree/MistralLite-AWQ-32g-4b-GEMM) | 4.3 GB | 32 | 4 | GEMM |
+
+## Dependencies
+- [`autoawq==0.2.5`](https://pypi.org/project/autoawq/0.2.5/) – [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) was used to quantize the MistralLite model.
+- [`vllm==0.4.2`](https://pypi.org/project/vllm/0.4.2/) – [vLLM](https://github.com/vllm-project/vllm) was used to host models for benchmarking.
+
+## Evaluations
+
+### Long Context
+
+The following benchmark results are shown as _accuracy_ (%) values, unless stated otherwise.
+
+#### Topic Retrieval
+
+See https://lmsys.org/blog/2023-06-29-longchat/
+
+| Model Name                                         |   n_topics=05 |   n_topics=10 |   n_topics=15 |   n_topics=20 |   n_topics=25 |
+|:---------------------------------------------------|--------------:|--------------:|--------------:|--------------:|--------------:|
+| _n_tokens_ (approx.) =         | _3048_ | _5966_ | _8903_ | _11832_ | _14757_ |
+| MistralLite                                        |           100 |           100 |           100 |           100 |            98 |
+| **MistralLite-AWQ**           |          **100** |          **100** |          **100**|          **100** |           **98** |
+| **MistralLite-AWQ-64g-4b-GEMM**            |          **100** |          **100** |          **100**|          **100** |           **98** |
+| **MistralLite-AWQ-32g-4b-GEMM**            |          **100** |          **100** |          **100**|          **100** |           **98** |
+| Mistral-7B-Instruct-v0.1                           |            96 |            52 |             2 |             0 |             0 |
+| Mistral-7B-Instruct-v0.2                           |           100 |           100 |           100 |           100 |           100 |
+| Mixtral-8x7B-v0.1                                  |             0 |             0 |             0 |             0 |             0 |
+| Mixtral-8x7B-Instruct-v0.1                         |           100 |           100 |           100 |           100 |           100 |
+
+#### [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results)
+
+See https://lmsys.org/blog/2023-06-29-longchat/#longeval-results
+
+| Model Name                                         |   n_lines=200 |   n_lines=300 |   n_lines=400 |   n_lines=500 |   n_lines=600 |   n_lines=680 |
+|:----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
+| _n_tokens_ (approx.) =         | _4317_ | _6415_ | _8510_ | _10610_ | _12698_ | _14373_ | 
+| MistralLite                                        |           100 |            94 |            86 |            82 |            76 |            66 |
+| **MistralLite-AWQ**           |           **96**|           **94**|           **88** |           **80** |           **70**|           **62** |
+| **MistralLite-AWQ-64g-4b-GEMM**            |           **96**|           **96**|           **90** |           **70** |           **72**|           **60** |
+| **MistralLite-AWQ-32g-4b-GEMM**            |           **98**|           **96**|           **84** |           **76** |           **70**|           **62** |
+| Mistral-7B-Instruct-v0.1                           |            96 |            56 |            38 |            36 |            30 |            30 |
+| Mistral-7B-Instruct-v0.2                           |           100 |           100 |            96 |            98 |            96 |            84 |
+| Mixtral-8x7B-v0.1                                  |            54 |            38 |            56 |            66 |            62 |            38 |
+| Mixtral-8x7B-Instruct-v0.1                         |           100 |           100 |           100 |           100 |           100 |           100 |
+
+#### Pass Key Retrieval
+
+See https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101
+
+| Model Name                               |   n_garbage=12000 |   n_garbage=20000 |   n_garbage=31000 |   n_garbage=38000 |   n_garbage=45000 | n_garbage=60000 |
+|:----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
+| _n_tokens_ (approx.) =         | _3272_ | _5405_ | _8338_ | _10205_ | _12071_ | _16072_ |
+| MistralLite                              |               100 |               100 |               100 |               100 |               100 | 100|
+| **MistralLite-AWQ** |              **100** |             **100**|              **100**|              **100** |              **100**| **100**|
+| **MistralLite-AWQ-64g-4b-GEMM**  |              **100** |             **100**|              **100**|              **100** |              **100**| **100**|
+| **MistralLite-AWQ-32g-4b-GEMM**  |              **100** |             **100**|              **100**|              **100** |              **100**| **100**|
+| Mistral-7B-Instruct-v0.1                            |               100 |                50 |                30 |                20 |                10 |                10 |
+| Mistral-7B-Instruct-v0.2                            |               100 |               100 |               100 |               100 |               100 |               100 |
+| Mixtral-8x7B-v0.1                                   |               100 |               100 |               100 |               100 |               100 |               100 |
+| Mixtral-8x7B-Instruct-v0.1                          |               100 |               100 |               100 |                90 |               100 |               100 |
+
+
+#### QuALITY (Question Answering with Long Input Texts, Yes!)
+
+See https://nyu-mll.github.io/quality/
+
+|Model Name| Test set Accuracy | Hard subset Accuracy|
+|:----------|-------------:|-------------:|
+| MistralLite                              |   56.8 |       74.5 |
+| **MistralLite-AWQ** |  **55.3** |      **71.8** |
+| **MistralLite-AWQ-64g-4b-GEMM**  |  **55.2** |      **72.9** |
+| **MistralLite-AWQ-32g-4b-GEMM**  |  **56.6** |      **72.8** |
+| Mistral-7B-Instruct-v0.1                 |   45.2 |       58.9 |
+| Mistral-7B-Instruct-v0.2                 |   55.5 |       74   |
+| Mixtral-8x7B-v0.1                        |   75   |       74.1 |
+| Mixtral-8x7B-Instruct-v0.1               |   68.7 |       83.3 |
+
+## Usage
+
+## Inference via vLLM HTTP Host
+
+### Launch Host
+```bash
+python -m vllm.entrypoints.openai.api_server \
+    --model amazon/MistralLite-AWQ \
+    --quantization awq
+```
+
+### Query Host
+```bash
+curl -X POST http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{ "model": "amazon/MistralLite-AWQ",
+          "prompt": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
+          "temperature": 0,
+          "echo": false
+    }'
+```
+
+## Inference via [vLLM Offline Inference](https://docs.vllm.ai/en/latest/getting_started/examples/offline_inference.html)
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+   "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
+]
+sampling_params = SamplingParams(temperature=0, max_tokens=100)
+
+llm = LLM(model="amazon/MistralLite-AWQ")
+
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+```
+
+## License
+
+Apache 2.0
+
+## Limitations
+
+Before using the MistralLite-AWQ model, it is important to perform your own
+independent assessment, and take measures to ensure that your use would comply
+with your own specific quality control practices and standards, and that your
+use would comply with the local rules, laws, regulations, licenses and terms
+that apply to you, and your content.

config.json

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+{
+  "_name_or_path": "amazon/MistralLite-AWQ",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "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": {
+    "bits": 4,
+    "group_size": 128,
+    "modules_to_not_convert": null,
+    "quant_method": "awq",
+    "version": "gemm",
+    "zero_point": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.40.2",
+  "use_cache": true,
+  "vocab_size": 32003
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.2"
+}

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special_tokens_map.json

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+}