add model card

README.md

@@ -1,3 +1,122 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- mistral
+- trl
+- sft
+- quantized
+- 4-bit
+- AWQ
+- text-generation
+- autotrain_compatible
+- endpoints_compatible
+- chatml
+model_creator: macadeliccc
+model_name: AlphaHitchhiker-7B-v2
+model_type: mistral
+pipeline_tag: text-generation
+inference: false
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
+quantized_by: Suparious
 ---
+# macadeliccc/AlphaHitchhiker-7B-v2 AWQ
+
+**UPLOAD IN PROGRESS**
+
+- Model creator: [macadeliccc](https://huggingface.co/macadeliccc)
+- Original model: [AlphaHitchhiker-7B-v2](https://huggingface.co/macadeliccc/AlphaHitchhiker-7B-v2)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/1lL97kzuxqykXGUT6F593.png)
+
+## Model Summary
+
+Finetuned for 2 epochs instead of 1. This improved loss by roughly 15%
+
+This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+## How to use
+
+### Install the necessary packages
+
+```bash
+pip install --upgrade autoawq autoawq-kernels
+```
+
+### Example Python code
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+
+model_path = "solidrust/AlphaHitchhiker-7B-v2-AWQ"
+system_message = "You are Alpha, incarnated as a powerful AI."
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(model_path,
+                                          fuse_layers=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                          trust_remote_code=True)
+streamer = TextStreamer(tokenizer,
+                        skip_prompt=True,
+                        skip_special_tokens=True)
+
+# Convert prompt to tokens
+prompt_template = """\
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant"""
+
+prompt = "You're standing on the surface of the Earth. "\
+        "You walk one mile south, one mile west and one mile north. "\
+        "You end up exactly where you started. Where are you?"
+
+tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
+                  return_tensors='pt').input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(tokens,
+                                  streamer=streamer,
+                                  max_new_tokens=512)
+
+```
+
+### About AWQ
+
+AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
+
+AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
+
+It is supported by:
+
+- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
+- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
+- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
+
+## Prompt template: ChatML
+
+```plaintext
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```