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README.md

@@ -1,3 +1,154 @@
 ---
+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
+- generated_from_trainer
+model-index:
+  - name: Senzu-7B-v0.1
+    results: []
 license: apache-2.0
+base_model: mistralai/Mistral-7B-v0.1
+datasets:
+  - Intel/orca_dpo_pairs
+  - NeuralNovel/Neural-Story-v1
+language:
+  - en
+quantized_by: Suparious
+pipeline_tag: text-generation
+model_creator: NeuralNovel
+model_name: Tiger 7B 0.1
+inference: false
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
 ---
+
+# Tiger 7B v0.1 AWQ
+
+- Model creator: [NeuralNovel](https://huggingface.co/NeuralNovel)
+- Original model: [Tiger-7B-v0.1](https://huggingface.co/NeuralNovel/Tiger-7B-v0.1)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/645cfe4603fc86c46b3e46d1/Z58bB5sYr3pyE2Ilbk7Dk.png)
+
+## Model Details
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+The following models were included in the merge:
+* [NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story](https://huggingface.co/NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story)
+* [NeuralNovel/Gecko-7B-v0.1-DPO](https://huggingface.co/NeuralNovel/Gecko-7B-v0.1-DPO)
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+
+slices:
+  - sources:
+      - model: NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
+        layer_range: [0, 32]
+      - model: NeuralNovel/Gecko-7B-v0.1-DPO
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+dtype: bfloat16
+
+## 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/Tiger-7B-v0.1-DPO-AWQ"
+system_message = "You are Tiger, 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
+```