add model card

README.md

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+tags:
+- finetuned
+- quantized
+- 4-bit
+- AWQ
+- transformers
+- pytorch
+- mistral
+- instruct
+- text-generation
+- conversational
+- license:apache-2.0
+- autotrain_compatible
+- endpoints_compatible
+- text-generation-inference
+- finetune
+- chatml
+- DPO
+model-index:
+  - name: Gecko-7B-v0.1-DPO
+    results: []
 license: apache-2.0
+base_model: NeuralNovel/Gecko-7B-v0.1
+datasets:
+  - Intel/orca_dpo_pairs
+language:
+  - en
+quantized_by: Suparious
+pipeline_tag: text-generation
+model_creator: NeuralNovel
+model_name: Gecko 7B 0.1 DPO
+inference: false
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
 ---
+
+# NeuralNovel/Gecko-7B-v0.1-DPO
+
+- Model creator: [NeuralNovel](https://huggingface.co/NeuralNovel)
+- Original model: [Gecko-7B-v0.1-DPO](https://huggingface.co/NeuralNovel/Gecko-7B-v0.1-DPO)
+
+![Gecko](https://i.ibb.co/gtf92Nt/OIG-43.jpg)
+
+## Model Summary
+
+Designed to generate instructive and narrative text, with a focus on mathematics & numeracy.
+
+Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2, with apache-2.0 license.
+
+You may download and use this model for research, training and commercial purposes.
+
+This model is suitable for commercial deployment.
+
+## 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/Gecko-7B-v0.1-DPO-AWQ"
+system_message = "You are Senzu, 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
+```