Update README.md

README.md

@@ -1,3 +1,103 @@
----
-license: llama3
----
+---
+license: llama3
+language:
+- en
+pipeline_tag: text-generation
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
+- meta-llama/Llama-3.3-70B-Instruct
+tags:
+- chat
+library_name: transformers
+---
+
+# Model Overview
+
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 1/28/2025
+
+Quantized version of [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) to FP8 data type, ready for inference with SGLang >= 0.3 or vLLM >= 0.5.2.
+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 are quantized. 
+
+## License
+
+We notice that [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) is licensed under MIT, however the original LLama model is licensed under llama-3. We are adopting llama-3 for this model to be consistent. 
+
+## Deployment
+
+### Use with SGLang
+
+```bash
+python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic \
+--port 30000 --host 0.0.0.0
+```
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+<details>
+<summary>Model Creation Code</summary>
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+
+MODEL_ID = "google/gemma-2-27b-it"
+
+# 1) Load model.
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, device_map="auto", torch_dtype="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# 2) 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="FP8_DYNAMIC", ignore=["lm_head"]
+)
+
+# 3) Apply quantization and save in compressed-tensors format.
+OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
+oneshot(
+    model=model,
+    recipe=recipe,
+    tokenizer=tokenizer,
+    output_dir=OUTPUT_DIR,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+```
+</details>
+
+## Evaluation
+
+TBA
+
+## Play Retail Mage
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64f908994110f1806f2c356a/vsWXpQqgHIqN4f4BM-RfS.png)
+
+[Retail Mage (Steam)](https://store.steampowered.com/app/3224380/Retail_Mage/) is an immersive sim that uses online LLM inference in almost all features in the gameplay! 
+
+Reviews
+
+“A true to life experience detailing how customer service really works.”
+10/10 – kpolupo
+
+“I enjoyed how many things were flammable in the store.”
+5/5 – mr_srsbsns
+
+“I've only known that talking little crow plushie in MageMart for a day and a half but if anything happened to him I would petrify everyone in this store and then myself.”
+7/7 – neondenki