Update README.md

README.md

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 ---
+tags:
+- fp8
+- vllm
+- medical
+- med
 license: other
 license_name: writer-open-model-license
 license_link: https://writer.com/legal/open-model-license/
+language: 
+- en
 ---
+
+# Palmyra-Med-70B-FP8
+This is a quantized version of [Palmyra-Med-70B](https://huggingface.co/Writer/Palmyra-Med-70B), which was developed by Writer.
+
+The original model performance on biomedical benchmarks is 85.87%. 
+**This quantized version acheives an average score of 85.62%.**
+
+## Model Overview:
+- **Model:** Llama based model finetuned to form Palmyra-X-004 and then again to form Palmyra-Med-70B.
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Intended Use Cases:** Palmyra-Med-70B is intended for non-commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
+- **License(s):** [writer-open-model-license](https://writer.com/legal/open-model-license/)
+
+### Writer Resources and Technical Documentation:
++ [Writer Blog](https://writer.com/blog/palmyra-med-fin-models/)
++ [Writer Developer Website](https://dev.writer.com/home/models)
++ [Writer AI Studio](https://writer.com/product/ai-studio/)
++ [Palmyra Model API](https://dev.writer.com/api-guides/chat-completion)
+
+### Model Optimizations
+
+[LLM_Compressor](https://github.com/vllm-project/llm-compressor) library.
+Using this optimization, the original FP16 weights and linear activations within the transformer blocks are adjusted to FP8, which decreases the model size and VRAM requirements by 50% overall.
+
+## Deployment with vLLM
+
+This model can be deployed using the [vLLM](https://docs.vllm.ai/en/latest/) library, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "bprice9/Palmyra-Med-70B-FP8"
+number_gpus = 2
+
+sampling_params = SamplingParams(temperature=0.5, top_p=0.9, max_tokens=512, stop_token_ids=[128001, 128009])
+
+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, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
+
+outputs = llm.generate(prompts, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+## Creation
+
+This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code below.
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.transformers.compression.helpers import (
+    calculate_offload_device_map,
+    custom_offload_device_map,
+)
+recipe = """
+quant_stage:
+    quant_modifiers:
+        QuantizationModifier:
+            ignore: ["lm_head"]
+            config_groups:
+                group_0:
+                    weights:
+                        num_bits: 8
+                        type: float
+                        strategy: tensor
+                        dynamic: false
+                        symmetric: true
+                    input_activations:
+                        num_bits: 8
+                        type: float
+                        strategy: tensor
+                        dynamic: false
+                        symmetric: true
+                    targets: ["Linear"]
+"""
+model_stub = "Writer/Palmyra-Med-70B"
+model_name = model_stub.split("/")[-1]
+device_map = calculate_offload_device_map(
+    model_stub, reserve_for_hessians=False, num_gpus=2, torch_dtype=torch.float16
+)
+model = SparseAutoModelForCausalLM.from_pretrained(
+    model_stub, torch_dtype=torch.float16, device_map=device_map
+)
+tokenizer = AutoTokenizer.from_pretrained(model_stub)
+output_dir = f"./{model_name}-FP8"
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+NUM_CALIBRATION_SAMPLES = 128
+MAX_SEQUENCE_LENGTH = 4096
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+ds = ds.map(preprocess)
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+oneshot(
+    model=model,
+    output_dir=output_dir,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    save_compressed=True,
+)
+```
+
+## Evaluation
+
+<table>
+  <tr>
+   <td style="width: 20%;"><strong>Biomedical Benchmark</strong>
+   </td>
+   <td style="width: 20%;"><strong>Med-PaLM-2 (5-shot)</strong>
+   </td>
+   <td style="width: 20%;"><strong>GPT-4</strong>
+   </td>
+   <td style="width: 20%;"><strong>Palmyra-Med-70B (Original FP16)</strong>
+   </td>
+   <td style="width: 20%;"><strong>Palmyra-Med-70B-FP8 (This Model)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU Clincal Knowledge
+   </td>
+   <td>88.3
+   </td>
+   <td>86.0
+   </td>
+   <td>90.9
+   </td>
+   <td>90.2
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU Medical Genetics
+   </td>
+   <td>90.0
+   </td>
+   <td>91.0
+   </td>
+   <td>94.0
+   </td>
+   <td>93.0
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU Anatomy
+   </td>
+   <td>77.8
+   </td>
+   <td>80.0
+   </td>
+   <td>83.7
+   </td>
+   <td>83.7
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU Professional Medicine
+   </td>
+   <td>95.2
+   </td>
+   <td>93.0
+   </td>
+   <td>92.7
+   </td>
+   <td>92.3
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU College Biology
+   </td>
+   <td>94.4
+   </td>
+   <td>95.1
+   </td>
+   <td>94.4
+   </td>
+   <td>93.8
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU College Medicine
+   </td>
+   <td>80.9
+   </td>
+   <td>76.9
+   </td>
+   <td>84.4
+   </td>
+   <td>84.4
+   </td>
+  </tr>
+  <tr>
+   <td>MedQA 4-options
+   </td>
+   <td>79.9
+   </td>
+   <td>78.9
+   </td>
+   <td>78.6
+   </td>
+   <td>79.5
+   </td>
+  </tr>
+  <tr>
+   <td>PubMed QA 
+   </td>
+   <td>79.2
+   </td>
+   <td>75.2
+   </td>
+   <td>79.6
+   </td>
+   <td>78.0
+   </td>
+  </tr>
+  <tr>
+  <tr>
+   <td>MedMCQA
+   </td>
+   <td>71.3
+   </td>
+   <td>69.5
+   </td>
+   <td>74.4
+   </td>
+   <td>75.7
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>84.1</strong>
+   </td>
+   <td><strong>82.8</strong>
+   </td>
+   <td><strong>85.9</strong>
+   </td>
+   <td><strong>85.6</strong>
+   </td>
+  </tr>
+</table>
+
+### Citation and Related Information Provided by Writer
+
+To cite this model:
+
+```
+@misc{Palmyra-Med-70B,
+  author = {Writer Engineering team},
+  title = {{Palmyra-Med-70b: A powerful LLM designed for healthcare}},
+  howpublished = {\url{https://dev.writer.com}},
+  year = 2024,
+  month = June 
+}
+```
+
+
+
+
+
+